play-2.ipynb

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   "source": [
    "# Reinforcement learning for compute graph topology and weight search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Headers"
   ]
  },
  {
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   "execution_count": 1,
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    "pycharm": {
     "name": "#%%\n"
    }
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   "outputs": [
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     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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   "source": [
    "from gym.utils import seeding\n",
    "%pylab inline\n",
    "import os \n",
    "os.environ['KERAS_BACKEND']='tensorflow'\n",
    "os.environ['THEANO_FLAGS'] = f'mode=FAST_RUN,device=cpu,floatX=float32'\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "from numpy import *\n",
    "from numpy.random import *\n",
    "import pandas as pd\n",
    "import random as rnd\n",
    "import keras\n",
    "import sys\n",
    "sys.path.append('/home/peter')\n",
    "sys.path.append('/home/ubuntu')\n",
    "#from universal import *\n",
    "sys.path.append('/home/peter/code/projects')\n",
    "sys.path.append('C:/Users/spook/Dropbox/code/projects')\n",
    "sys.path.append('/home/peter/code/projects/deepneat')\n",
    "sys.path.append('/home/ubuntu')\n",
    "sys.path.append('/home/ubuntu/new/automl')\n",
    "#from massimport import *\n",
    "#from project_common import *\n",
    "#from paramspace import *\n",
    "#import project_common\n",
    "import gym\n",
    "from aidevutil import *\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "#from deepneat import *\n",
    "from ipywidgets import interactive\n",
    "from scipy.optimize import minimize\n",
    "from sklearn.model_selection import *\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from dask import compute, delayed, persist\n",
    "from dask.distributed import Client, wait\n",
    "from dask.distributed import as_completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from empyrical import sortino_ratio, calmar_ratio, omega_ratio\n",
    "from stable_baselines.common.policies import MlpPolicy, MlpLstmPolicy, ActorCriticPolicy, FeedForwardPolicy\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, VecEnv, VecEnvWrapper\n",
    "from stable_baselines import A2C, PPO2, DQN, ACKTR, ACER#, TRPO\n",
    "from stable_baselines.common.vec_env import DummyVecEnv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "from IPython.display import clear_output\n",
    "import shutil\n",
    "import time\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "gc.enable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sigmoids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def revsigm(data):\n",
    "    return 1.-(1./(1.+np.exp(-data)))\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))\n",
    "\n",
    "def retanher(x):\n",
    "    if x > 0:\n",
    "        return np.tanh(x)\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "retanh = np.vectorize(retanher)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def Outputs(data):\n",
    "    return np.round(revsigm(data))\n",
    "    \n",
    "def MungeData(data):\n",
    "    # Sex\n",
    "    data.drop(['Ticket', 'Name'], inplace=True, axis=1)\n",
    "    data.Sex.fillna('0', inplace=True)\n",
    "    data.loc[data.Sex != 'male', 'Sex'] = 0\n",
    "    data.loc[data.Sex == 'male', 'Sex'] = 1\n",
    "    # Cabin\n",
    "    cabin_const = 1\n",
    "    data.Cabin.fillna(str(cabin_const), inplace=True)\n",
    "    data.loc[data.Cabin.str[0] == 'A', 'Cabin'] = cabin_const\n",
    "    data.loc[data.Cabin.str[0] == 'B', 'Cabin'] = cabin_const\n",
    "    data.loc[data.Cabin.str[0] == 'C', 'Cabin'] = cabin_const\n",
    "    data.loc[data.Cabin.str[0] == 'D', 'Cabin'] = cabin_const\n",
    "    data.loc[data.Cabin.str[0] == 'E', 'Cabin'] = cabin_const\n",
    "    data.loc[data.Cabin.str[0] == 'F', 'Cabin'] = cabin_const\n",
    "    data.loc[data.Cabin.str[0] == 'G', 'Cabin'] = cabin_const\n",
    "    data.loc[data.Cabin.str[0] == 'T', 'Cabin'] = cabin_const\n",
    "    # Embarked\n",
    "    data.loc[data.Embarked == 'C', 'Embarked'] = 1\n",
    "    data.loc[data.Embarked == 'Q', 'Embarked'] = 2\n",
    "    data.loc[data.Embarked == 'S', 'Embarked'] = 3\n",
    "    data.Embarked.fillna(0, inplace=True)\n",
    "    data.fillna(-1, inplace=True)\n",
    "    return data.astype(float)\n",
    "\n",
    "train = pd.read_csv(\"train.csv\", dtype={\"Age\": np.float64}, )\n",
    "test = pd.read_csv(\"test.csv\", dtype={\"Age\": np.float64}, )\n",
    "mdt = MungeData(train)\n",
    "\n",
    "bdy = mdt['Survived'].values\n",
    "bdx = mdt.drop(['Survived'], axis=1).values[:,1:]\n",
    "bdx_test_df = MungeData(test)\n",
    "x_val_real = bdx_test_df.values[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed()\n",
    "rnd.seed()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### nrows and split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "nrows = 1000\n",
    "nfeats = 4\n",
    "\n",
    "bdx = bdx[0:nrows]\n",
    "bdy = bdy[0:nrows]\n",
    "seed = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# sc = StandardScaler()\n",
    "# sc.fit(bdx)\n",
    "# bdx = sc.transform(bdx)\n",
    "# x_val_real = sc.transform(x_val_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "bdx, bdy = make_classification(n_samples=nrows, n_features=nfeats)\n",
    "\n",
    "bdx_val = bdx\n",
    "bdy_val = bdy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#bdx = np.tanh(bdx)\n",
    "#x_val_real = np.tanh(x_val_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-3.518956, -5.074841, -2.461901, -3.171352]),\n",
       " array([3.701187, 4.024875, 2.309978, 2.383362]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bdx, axis=0), np.max(bdx, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rtit = pd.read_csv('titanic_100p_score.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# cp = int(0.9 * bdx_test.shape[0])\n",
    "# bdx_val = bdx_test[cp:]\n",
    "# bdy_val = bdy_test[cp:]\n",
    "# bdx_test = bdx_test[0:cp]\n",
    "# bdy_test = bdy_test[0:cp]\n",
    "\n",
    "bdx_val = x_val_real\n",
    "bdy_val = rtit.values[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Num nodes and min/max weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "num_inputs = 4+1 #+ the bias\n",
    "num_outputs = 1\n",
    "\n",
    "max_nodes = num_inputs+num_outputs + 4\n",
    "max_links = 2400\n",
    "prefer_incoming_links = 3\n",
    "prefer_incoming_links_enabled = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "max_weight = 5\n",
    "max_init_weight = 2\n",
    "# if true, the new links will have random weights, otherwise init_structural_with\n",
    "random_init_structural = 1\n",
    "init_structural_with = 1.0\n",
    "# if true, some inputs can become disconnected\n",
    "enable_disconnected_inputs = 0\n",
    "\n",
    "# the last (bias) input\n",
    "bias_const = 2.0\n",
    "enforce_bias_link = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "m = zeros((max_nodes,max_nodes))\n",
    "m[0:num_inputs, num_inputs:num_inputs+num_outputs] = 1\n",
    "m[3, 8] = 0\n",
    "\n",
    "wm = randn(max_nodes, max_nodes)\n",
    "#m, wm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(m);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### funcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funcs = ['add', 'mul', #'inv', 'neg', 'abs', \n",
    "         #'div', 'arctan2', 'hypot', 'pow', 'sub', 'mod', 'hside', # all of these have arity 2\n",
    "      'sqrt', 'sqr', \n",
    "#       'sig',\n",
    "       'tanh',\n",
    "       'cbrt', 'cube', \n",
    "      'min', 'max', 'mean', 'span',\n",
    "#      'std', 'var', \n",
    "#      'argmin', 'argmax', 'sign', \n",
    "      'exp', 'log',  \n",
    "      'sin', 'cos', #'tan', 'arcsin', 'arccos', 'arctan', \n",
    "#      'sinh', 'cosh', 'tanh', 'arcsinh', 'arccosh', 'arctanh',\n",
    "        ]\n",
    "len(funcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# the node matrix\n",
    "# row - node idx\n",
    "# col - func\n",
    "nm = zeros((max_nodes-num_inputs, len(funcs)))\n",
    "nm[:,0] = 1 # all are \"add\" initially"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def num_nodes(m):\n",
    "    sml = (np.sum(m[:,num_inputs:], axis=0) > 0).tolist()[::-1]\n",
    "    if 1.0 not in sml:\n",
    "        return num_inputs + num_outputs\n",
    "    else:\n",
    "        return (max_nodes - num_inputs - num_outputs - \n",
    "                sml.index(1)) + num_inputs + num_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def see_graph(m, pos=None, wm=None, nm=None):\n",
    "    \n",
    "    inpout_list = list(range(num_inputs))+list(range(num_inputs+num_outputs))\n",
    "    \n",
    "    g = nx.DiGraph(m)\n",
    "    #g.remove_nodes_from(nx.isolates(g))\n",
    "    g.remove_nodes_from([x for x in list(nx.isolates(g)) if x not in inpout_list])\n",
    "    if pos is None:\n",
    "        pos = nx.spring_layout(g,k=50)\n",
    "        \n",
    "    if wm is not None:\n",
    "        for i in range(max_nodes):\n",
    "            for j in range(max_nodes):\n",
    "                if m[i,j] == 1:\n",
    "                    g[i][j]['weight'] = wm[i,j]\n",
    "                    \n",
    "    nlabs = {}\n",
    "    for i in range(max_nodes):\n",
    "        if i in g.nodes():\n",
    "            nlabs[i] = str(i)\n",
    "    if nm is not None:\n",
    "        for i in range(num_inputs, num_nodes(m)):\n",
    "            if i in g.nodes():\n",
    "                #g.node[i]['mm'] = funcs[argmax(nm[i-num_inputs,:].reshape(-1))]\n",
    "                nlabs[i] = str(i) + '\\n' + funcs[argmax(nm[i-num_inputs,:].reshape(-1))]\n",
    "            \n",
    "     \n",
    "    cls = []\n",
    "    if wm is not None:\n",
    "        for i,o in g.edges():\n",
    "            cls.append( g.adj[i][o]['weight'] )\n",
    "\n",
    "    nx.draw_networkx_nodes(g, pos=pos, node_size=800)\n",
    "    nx.draw_networkx_labels(g, pos=pos, labels=nlabs, font_size=10);\n",
    "\n",
    "    if cls:\n",
    "        nx.drawing.nx_pylab.draw_networkx_edges(g, pos=pos, \n",
    "                               edge_vmin = -max_weight, \n",
    "                               edge_vmax = max_weight, \n",
    "                               edge_cmap = mpl.cm.get_cmap(name='jet'#'RdBu'\n",
    "                                                          ), \n",
    "                               width=1, edge_color=cls, \n",
    "                               alpha=1.0, \n",
    "                               arrows=1,\n",
    "                               node_size=800,\n",
    "                               arrowstyle='fancy', \n",
    "                               arrowsize=12\n",
    "                              );\n",
    "    else:\n",
    "        nx.drawing.nx_pylab.draw_networkx_edges(g, pos=pos, \n",
    "                               width=1,\n",
    "                               alpha=0.75, \n",
    "                               arrows=1, \n",
    "                              );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#see_graph(m,wm=wm, nm=nm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### is_ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def is_ok(m, num_inputs, num_outputs, tell=0):\n",
    "    \n",
    "    inpout_list = list(range(num_inputs))+list(range(num_inputs+num_outputs))\n",
    "\n",
    "    if np.sum(m) == 0:\n",
    "        if tell: print('no edges')\n",
    "        return False \n",
    "\n",
    "    g = nx.Graph(m)\n",
    "    #g.remove_nodes_from(nx.isolates(g))\n",
    "    g.remove_nodes_from([x for x in list(nx.isolates(g)) \n",
    "                         if x not in inpout_list])\n",
    "    \n",
    "    if enable_disconnected_inputs:\n",
    "        trl = list(nx.connected_components(g))\n",
    "        trl = [x for x in trl if not (len(x)==1 and all([y in inpout_list for y in x]))]\n",
    "        if len(trl) > 1:\n",
    "            if tell: print('is not connected')\n",
    "            return False\n",
    "    else:\n",
    "        if len(list(nx.connected_components(g))) > 1:\n",
    "            if tell: print('is not connected')\n",
    "            return False\n",
    "    \n",
    "    \n",
    "    g = nx.DiGraph(m)\n",
    "    g.remove_nodes_from(list(nx.isolates(g)))\n",
    "    if not nx.dag.is_directed_acyclic_graph(g):\n",
    "        if tell: print('is not DAG')\n",
    "        return False\n",
    "    \n",
    "    # make sure the remaining nodes will be connected \n",
    "    inps=list(range(num_inputs)) \n",
    "    outs=list(range(num_inputs, num_inputs+num_outputs))\n",
    "    for k in g.nodes():\n",
    "        if k in inps:\n",
    "            # input\n",
    "            if (len(list(g.out_edges(nbunch=k))) == 0):\n",
    "                if tell: print('input',k, 'is not connected')\n",
    "                return False\n",
    "        elif k in outs:\n",
    "            # output\n",
    "            if (len(list(g.in_edges(nbunch=k))) == 0):\n",
    "                if tell: print('output',k, 'is not connected')\n",
    "                return False\n",
    "        else:\n",
    "            # hidden\n",
    "            if (len(list(g.in_edges(nbunch=k))) == 0 ):\n",
    "                if tell: print('node',k, 'has no incoming edges')\n",
    "                return False\n",
    "                \n",
    "            if (len(list(g.out_edges(nbunch=k))) == 0):\n",
    "                if tell: print('node',k, 'has no outgoing edges')\n",
    "                return False\n",
    "            \n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# add link\n",
    "def add_link(m, si, di, num_inputs, num_outputs, max_links, wm=None):\n",
    "    # outputs can't be sources\n",
    "    if si in list(range(num_inputs, num_inputs+num_outputs)):\n",
    "        return False\n",
    "    # inputs can't be destinations\n",
    "    if di in list(range(num_inputs)):\n",
    "        return False\n",
    "    \n",
    "    if np.sum(m) >= max_links:\n",
    "        return False\n",
    "    \n",
    "    tm = np.copy(m)\n",
    "    tm[si, di] = 1\n",
    "    \n",
    "    if not is_ok(tm, num_inputs, num_outputs):\n",
    "        #print(\"Not acyclic in add_link!\")\n",
    "        return False\n",
    "\n",
    "    m[si, di] = 1\n",
    "    \n",
    "    if wm is not None: # the weight matrix\n",
    "        if random_init_structural:\n",
    "            wm[si, di] = rnd.uniform(-max_init_weight, max_init_weight)\n",
    "        else:\n",
    "            wm[si, di] = init_structural_with\n",
    "        \n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# remove link\n",
    "def remove_link(m, si, di, num_inputs, num_outputs, wm=None):\n",
    "    \n",
    "    # don't allow removing links coming from bias\n",
    "    if enforce_bias_link:\n",
    "        if si == num_inputs-1:\n",
    "            return False\n",
    "    \n",
    "    tm = np.copy(m)\n",
    "    tm[si, di] = 0\n",
    "    \n",
    "    if not is_ok(tm, num_inputs, num_outputs):\n",
    "        #print(\"Not acyclic in remove_link!\")\n",
    "        return False\n",
    "            \n",
    "    m[si, di] = 0\n",
    "    if wm is not None: # the weight matrix\n",
    "        wm[si, di] = 0\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# add node\n",
    "def add_node(m, si, di, num_inputs, num_outputs, max_links, wm=None, nm=None):\n",
    "    if m[si, di] == 0:\n",
    "        return False # can't split nonexistent connections\n",
    "    \n",
    "    # don't split bias links\n",
    "    if enforce_bias_link:\n",
    "        if si == num_inputs-1:\n",
    "            return False\n",
    "    \n",
    "    if np.sum(m) >= max_links-1:\n",
    "        return False\n",
    "    \n",
    "#     if (si, di) not in innovs.innovs:\n",
    "#         # new innovation\n",
    "#         inn = innovs.last_innov_num\n",
    "#         innovs.innovs[(si, di)] = inn\n",
    "#         innovs.last_innov_num += 1\n",
    "#     else:\n",
    "#         # known\n",
    "#         inn = innovs.innovs[(si, di)]\n",
    "    \n",
    "    if not any (np.sum(m[:,num_inputs+num_outputs:], axis=0) == 0):\n",
    "        return False\n",
    "    else:\n",
    "        # there is a free slot, use that\n",
    "        inn = argmin(np.sum(m[:,num_inputs+num_outputs:], axis=0)) + num_inputs + num_outputs\n",
    "    \n",
    "    m[si, di] = 0\n",
    "    m[si, inn] = 1\n",
    "    m[inn, di] = 1\n",
    "    \n",
    "    # hack\n",
    "    # always add bias connection\n",
    "    if enforce_bias_link:\n",
    "        if si != num_inputs-1:\n",
    "            m[num_inputs-1, inn] = 1\n",
    "    \n",
    "    if wm is not None:\n",
    "        wm[si, di] = 0\n",
    "        if random_init_structural:\n",
    "            wm[si, inn] = rnd.uniform(-max_init_weight,max_init_weight)\n",
    "            wm[inn, di] = rnd.uniform(-max_init_weight,max_init_weight)\n",
    "            if enforce_bias_link:\n",
    "                if si != num_inputs-1: \n",
    "                    wm[num_inputs-1, inn] = rnd.uniform(-max_init_weight,max_init_weight)\n",
    "        else:\n",
    "            wm[si, inn] = init_structural_with\n",
    "            wm[inn, di] = init_structural_with\n",
    "            if enforce_bias_link:\n",
    "                if si != num_inputs-1: \n",
    "                    wm[num_inputs-1, inn] = init_structural_with\n",
    "        \n",
    "    if nm is not None:\n",
    "        # random func\n",
    "        if random_init_structural:\n",
    "            ri = rnd.choice(arange(len(funcs)))\n",
    "        else:\n",
    "            ri = 0\n",
    "        nm[inn-num_inputs, :] = 0\n",
    "        nm[inn-num_inputs, ri] = 1\n",
    "        \n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# remove a simple node (connected by 2 edges only)\n",
    "def remove_node(m, ni, num_inputs, num_outputs, wm=None, nm=None):\n",
    "    # also ensure only one incoming & outgoing edge to ni\n",
    "    if (np.sum(m[:,ni]) != 1) or (np.sum(m[ni, :]) != 1):\n",
    "        return False\n",
    "    \n",
    "    si = argmax(m[:,ni])\n",
    "    di = argmax(m[ni, :])\n",
    "    \n",
    "    # can't remove inputs/outputs\n",
    "    if ni < num_inputs+num_outputs:\n",
    "        return False\n",
    "    \n",
    "    tm = np.copy(m)\n",
    "    tm[si, ni] = 0\n",
    "    tm[ni, di] = 0\n",
    "    tm[si, di] = 1\n",
    "    \n",
    "    if not is_ok(tm, num_inputs, num_outputs):\n",
    "        #print(\"Not acyclic in remove_node!\")\n",
    "        return False\n",
    "    \n",
    "    m[si, ni] = 0\n",
    "    m[ni, di] = 0\n",
    "    m[si, di] = 1\n",
    "    \n",
    "    if wm is not None:\n",
    "        wm[si, ni] = 0\n",
    "        wm[ni, di] = 0\n",
    "        if wm[si, di] == 0:\n",
    "            if random_init_structural:\n",
    "                wm[si, di] = rnd.uniform(-max_init_weight,max_init_weight)\n",
    "            else:\n",
    "                wm[si, di] = init_structural_with\n",
    "            \n",
    "    if nm is not None:\n",
    "        nm[ni-num_inputs, :] = 0\n",
    "        \n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input/output description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inputs to the agent:\n",
    "\n",
    "- The adjacency matrix's operating field\n",
    "- The weight matrix\n",
    "- The link caret x/y position\n",
    "- What's under the link caret position (link or not)\n",
    "- What weight is under the caret position (0 if no link)\n",
    "- The node matrix\n",
    "- The node caret position\n",
    "- What's under the node caret\n",
    "- Amount of links / limit\n",
    "- Amount of nodes / limit\n",
    "- Sum of link caret row / num_nodes\n",
    "- Sum of link caret col / num_nodes\n",
    "- Link caret is at diagonal or not\n",
    "- Node removal is possible at link caret position or not\n",
    "- Last action was successful or not\n",
    "- Last score/reward\n",
    "- Charge for adding a node \n",
    "- Charge for adding a link"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possible actions for the agent:\n",
    "\n",
    "- Move link caret up\n",
    "- Move link caret down\n",
    "- Move link caret left\n",
    "- Move link caret right\n",
    "- Randomize link caret position\n",
    "\n",
    "- Move node caret up\n",
    "- Move node caret down\n",
    "- Randomize node caret\n",
    "\n",
    "- Attempt to add link at caret position\n",
    "- Attempt to remove link at caret position\n",
    "- Attempt to add node at caret position\n",
    "- Attempt to remove node at caret position\n",
    "\n",
    "- Mutate weight at link caret position +\n",
    "- Mutate weight at link caret position -\n",
    "- Randomize weight at link caret position\n",
    "- Set weight to 1.0 at link caret position\n",
    "\n",
    "- Mutate node at node caret position +\n",
    "- Mutate node at node caret position -\n",
    "- Randomize node at node caret position\n",
    "- Set node to default at node caret position\n",
    "\n",
    "- Save state\n",
    "- Restore state\n",
    "\n",
    "- Request score update (and reward)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possible actions for the agent when the env type is Box\n",
    "\n",
    "- Link caret row (Y) movement (-1 < move up < -0.33 < stop < 0.33 < move down < 1)\n",
    "- Link caret col (X) movement (-1 < move left < -0.33 < stop < 0.33 < move right < 1)\n",
    "- Node caret movement (-1 < move up < -0.33 < stop < 0.33 < move down < 1)\n",
    "- Link add/remove decision (-1 < remove < -0.33 < do nothing < 0.33 < add < 1)\n",
    "- Node add/remove decision (-1 < remove < -0.33 < do nothing < 0.33 < add < 1)\n",
    "\n",
    "- Weight movement at link caret (-1 < scaled- < -0.2 < no movement < 0.2 < scaled < 1)\n",
    "or maybe\n",
    "- What weight it wants at that caret position (if there is link) [-1,1] -> [-max_weight, max_weight]\n",
    "\n",
    "- Node parameter movement at node caret (-1 < move left < -0.33 < stop < 0.33 < move right < 1)\n",
    "or maybe\n",
    "- What parameter it wants there, discretized to int of given parameter list size  \n",
    "\n",
    "7 total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action_codes = [\n",
    "\"Move link caret up\",\n",
    "\"Move link caret down\",\n",
    "\"Move link caret left\",\n",
    "\"Move link caret right\",\n",
    "#\"Randomize link caret position\",\n",
    "\n",
    "\"Move node caret up\",\n",
    "\"Move node caret down\",\n",
    "#\"Randomize node caret\",\n",
    "\n",
    "\"Attempt to add link at caret position\",\n",
    "\"Attempt to remove link at caret position\",\n",
    "\"Attempt to add node at caret position\",\n",
    "\"Attempt to remove node at caret position\",\n",
    "\n",
    "\"Mutate weight at link caret position +\",\n",
    "\"Mutate weight at link caret position -\",\n",
    "#\"Randomize weight at link caret position\",\n",
    "#\"Set weight to 1.0 at link caret position\",\n",
    "\n",
    "\"Mutate node at node caret position +\",\n",
    "\"Mutate node at node caret position -\",\n",
    "#\"Randomize node at node caret position\",\n",
    "#\"Set node to default at node caret position\"\n",
    "\"Request testing\"\n",
    "]\n",
    "\n",
    "len(action_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def make_input(m, wm, nm, caret_row, caret_col, node_caret,\n",
    "               max_nodes, max_links, \n",
    "               num_inputs, num_outputs,\n",
    "               la=0, ls=0, nnc=0, nlc=0):\n",
    "    iii = []\n",
    "    # The adjacency matrix's operating field\n",
    "    iii += [m[:,num_inputs:].reshape(-1) ]\n",
    "    # The weight matrix\n",
    "    iii += [wm[:,num_inputs:].reshape(-1) / max_weight] \n",
    "    # The link caret x/y position\n",
    "    i = np.zeros(max_nodes)\n",
    "    i[caret_row] = 1\n",
    "    iii += [i]\n",
    "    i = np.zeros(max_nodes)\n",
    "    i[caret_col] = 1\n",
    "    iii += [i]\n",
    "    # What's under the link caret position (link or not)\n",
    "    iii += [array([m[caret_row, caret_col]])]\n",
    "    # What weight is under the caret position (0 if no link)\n",
    "    iii += [array([wm[caret_row, caret_col]])]\n",
    "    # The node matrix\n",
    "    iii += [nm.reshape(-1)]\n",
    "    # The node caret position\n",
    "    i = np.zeros(max_nodes-num_inputs)\n",
    "    i[node_caret]=1\n",
    "    iii += [i]\n",
    "    # What's under the node caret\n",
    "    iii += [nm[node_caret,:].reshape(-1)]\n",
    "    # Amount of links / limit\n",
    "    iii += [array([np.sum(m) / max_links])]\n",
    "    # Amount of nodes / limit\n",
    "    iii += [array([num_nodes(m)/max_nodes])]\n",
    "    # Sum of link caret row / num_nodes\n",
    "    iii += [array([np.sum(m[caret_row, :]) / num_nodes(m)])]\n",
    "    # Sum of link caret col / num_nodes\n",
    "    iii += [array([np.sum(m[:, caret_col]) / num_nodes(m)])]\n",
    "    # Link caret is at diagonal or not\n",
    "    if caret_row == caret_col:\n",
    "        iii += [array([1])]\n",
    "    else:\n",
    "        iii += [array([0])]\n",
    "    # Node removal is possible at link caret position or not\n",
    "    if (caret_row == caret_col) and ((sum(m[:,caret_col]) == 1) and (sum(m[caret_row, :]) == 1)):\n",
    "        iii += [array([1])]\n",
    "    else:\n",
    "        iii += [array([0])]\n",
    "        \n",
    "    # Last action was successful or not\n",
    "    iii += [array([la])]\n",
    "    \n",
    "    # Last score\n",
    "    iii += [array([ls])]\n",
    "    \n",
    "    # charge for nodes\n",
    "    iii += [array([nnc])]\n",
    "\n",
    "    # charge for links\n",
    "    iii += [array([nlc])]\n",
    "    \n",
    "    inp = hstack(iii)\n",
    "    return inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exmpi = make_input(m, m, nm, 0, 2, 0, max_nodes, max_links, num_inputs, num_outputs)\n",
    "exmpi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### activate graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def activate_graph(gr, inputs, num_outputs=1): \n",
    "    num_inputs = inputs.shape[0]\n",
    "    allnodes = list(nx.dfs_postorder_nodes(gr))[::-1]\n",
    "    for a in allnodes: gr.node[a]['act'] = None\n",
    "    \n",
    "    # separate input from non-input nodes\n",
    "    allnodes = [x for x in allnodes if x > (num_inputs-1)]\n",
    "    \n",
    "    # input the data\n",
    "    for i,inp in zip(range(0, num_inputs), inputs): \n",
    "        gr.node[i]['act'] = inp\n",
    "        \n",
    "    # pass through the graph\n",
    "    for an in allnodes:\n",
    "        # collect the inputs to this node\n",
    "        mm = gr.node[an]['mm']\n",
    "        \n",
    "        # also sort the incoming edges by id for consistency\n",
    "        inedg = list(gr.in_edges(nbunch=an))\n",
    "        \n",
    "        #inedg = sorted(inedg, key = lambda x: x[0])\n",
    "        \n",
    "        inps = [gr.node[i]['act'] for i,o in inedg]\n",
    "        inedgw = list(gr.in_edges(nbunch=an, data=True))\n",
    "        \n",
    "        #print(inedgw)\n",
    "        ws = [ts['weight'] for i,o,ts in inedgw]\n",
    "        # weighted stack\n",
    "        inps = np.vstack([w*x for w,x in zip(ws, inps)])\n",
    "        \n",
    "        sact = np.sum(inps, axis=0) # this node's default activation\n",
    "        act = sact\n",
    "\n",
    "        try:\n",
    "            if mm=='add':\n",
    "                act = sact\n",
    "            if mm=='neg':\n",
    "                act = -sact\n",
    "            if mm=='mul':\n",
    "                act = np.prod(inps, axis=0)\n",
    "            if mm=='inv':\n",
    "                act = 1.0 / sact\n",
    "            if mm=='sqr':\n",
    "                act = sact**2\n",
    "            if mm=='cube':\n",
    "                act = sact**3\n",
    "            if mm=='sqrt':\n",
    "                act = np.sqrt(sact)\n",
    "            if mm=='sig' or mm=='sigmoid':\n",
    "                act = sigmoid(sact)\n",
    "            if mm=='cbrt':\n",
    "                act = np.cbrt(sact)\n",
    "            if mm=='sin':\n",
    "                act = np.sin(sact)\n",
    "            if mm=='cos':\n",
    "                act = np.cos(sact)\n",
    "            if mm=='tan':\n",
    "                act = np.tan(sact)\n",
    "            if mm=='arcsin':\n",
    "                act = np.arcsin(sact)\n",
    "            if mm=='arccos':\n",
    "                act = np.arccos(sact)\n",
    "            if mm=='arctan':\n",
    "                act = np.arctan(sact)\n",
    "            if mm=='log':\n",
    "                act = np.log(sact)\n",
    "            if mm=='exp':\n",
    "                act = np.exp(sact)\n",
    "            if mm=='abs':\n",
    "                act = np.abs(sact)\n",
    "            if mm=='sinh':\n",
    "                act = np.sinh(sact)\n",
    "            if mm=='cosh':\n",
    "                act = np.cosh(sact)\n",
    "            if mm=='tanh':\n",
    "                act = np.tanh(sact)\n",
    "            if mm=='arcsinh':\n",
    "                act = np.arcsinh(sact)\n",
    "            if mm=='arccosh':\n",
    "                act = np.arccosh(sact)\n",
    "            if mm=='arctanh':\n",
    "                act = np.arctanh(sact)\n",
    "            if mm=='min':\n",
    "                act = np.min(inps, axis=0)\n",
    "            if mm=='max':\n",
    "                act = np.max(inps, axis=0)\n",
    "            if mm=='mean':\n",
    "                act = np.mean(inps, axis=0)\n",
    "            if mm=='span':\n",
    "                act = np.max(inps, axis=0) - np.min(inps, axis=0)\n",
    "            if mm=='var':\n",
    "                act = np.var(inps, axis=0)\n",
    "            if mm=='std':\n",
    "                act = np.std(inps, axis=0)\n",
    "            if mm=='argmax':\n",
    "                act = np.argmax(inps, axis=0) / inps.shape[0] # normalized argmax\n",
    "            if mm=='argmin':\n",
    "                act = np.argmin(inps, axis=0) / inps.shape[0] # normalized argmax                \n",
    "            if mm=='sign':\n",
    "                act = np.sign(sact)\n",
    "            # arity 2\n",
    "            if mm=='div':\n",
    "                if inps.shape[0]>1:\n",
    "                    act = inps[0]/inps[1]\n",
    "                else:\n",
    "                    act = inps[0]\n",
    "            if mm=='arctan2':\n",
    "                if inps.shape[0]>1:\n",
    "                    act = np.arctan2(inps[0],inps[1])\n",
    "                else:\n",
    "                    act = inps[0]\n",
    "            if mm=='hypot':\n",
    "                if inps.shape[0]>1:\n",
    "                    act = np.hypot(inps[0],inps[1])\n",
    "                else:\n",
    "                    act = inps[0]\n",
    "            if mm=='pow':\n",
    "                if inps.shape[0]>1:\n",
    "                    act = np.power(inps[0],inps[1])\n",
    "                else:\n",
    "                    act = inps[0]\n",
    "            if mm=='sub':\n",
    "                if inps.shape[0]>1:\n",
    "                    act = np.subtract(inps[0],inps[1])\n",
    "                else:\n",
    "                    act = inps[0]\n",
    "            if mm=='mod':\n",
    "                if inps.shape[0]>1:\n",
    "                    act = np.mod(inps[0],inps[1])\n",
    "                else:\n",
    "                    act = inps[0]\n",
    "            if mm=='hside':\n",
    "                if inps.shape[0]>1:\n",
    "                    act = np.heaviside(inps[0],inps[1])\n",
    "                else:\n",
    "                    act = inps[0]\n",
    "        \n",
    "        except Exception as ex:\n",
    "            print(ex)\n",
    "            act = sact\n",
    "\n",
    "        # apply slope and bias\n",
    "        #if use_bias: act = act + bias\n",
    "        #act = act.reshape(1,-1)\n",
    "        \n",
    "        #act = sigmoid(sact)\n",
    "        \n",
    "        # store activation\n",
    "        gr.node[an]['act'] = act \n",
    "        \n",
    "    #print('act:', act)\n",
    "    outputs = [gr.node[o]['act'] for o in allnodes[-num_outputs:]]\n",
    "    return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_node(m, 0, 8, num_inputs, num_outputs, max_links, wm=wm)\n",
    "add_node(m, 9, 8, num_inputs, num_outputs, max_links, wm=wm)\n",
    "add_node(m, 0, 9, num_inputs, num_outputs, max_links, wm=wm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "inpout_list = list(range(num_inputs))+list(range(num_inputs+num_outputs))\n",
    "g = nx.DiGraph(m)\n",
    "g.remove_nodes_from([x for x in list(nx.isolates(g)) if x not in inpout_list])\n",
    "\n",
    "for i in range(max_nodes):\n",
    "    for j in range(max_nodes):\n",
    "        if m[i,j] != 0:\n",
    "            g.edges[i,j]['weight'] = wm[i,j]\n",
    "\n",
    "for i in range(num_inputs, num_nodes(m)):\n",
    "    if i in g.nodes():\n",
    "        g.node[i]['mm'] = funcs[argmax(nm[i-num_inputs,:].reshape(-1))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=activate_graph(g, vstack([bdx.T, ones(bdx.shape[0])*25]), \n",
    "                 num_outputs=num_outputs)[0]\n",
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(isnan(bdx_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### score metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "score_metric = accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#max_error = mean_squared_error(~bdy_val+2, bdy_val)\n",
    "#max_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def test_xor(m, wm, nm, num_inputs, num_outputs, max_nodes):\n",
    "    inpout_list = list(range(num_inputs))+list(range(num_inputs+num_outputs))\n",
    "    g = nx.DiGraph(m)\n",
    "    g.remove_nodes_from([x for x in list(nx.isolates(g)) \n",
    "                         if x not in inpout_list])\n",
    "\n",
    "    for i in range(max_nodes):\n",
    "        for j in range(max_nodes):\n",
    "            if m[i,j] == 1:\n",
    "                g.edges[i,j]['weight'] = wm[i,j]\n",
    "\n",
    "    for i in range(num_inputs, num_nodes(m)):\n",
    "        if i in g.nodes():\n",
    "            g.node[i]['mm'] = funcs[argmax(nm[i-num_inputs,:].reshape(-1))]\n",
    "            \n",
    "    if 1:\n",
    "        p = activate_graph(g, vstack([bdx.T, ones(bdx.shape[0])*bias_const]), \n",
    "                           num_outputs=num_outputs)[0]\n",
    "                   \n",
    "        t = ((np.tanh(p)+1.0)/2)#(p-np.min(p))/(np.max(p) - np.min(p))\n",
    "        if any(isnan(t)): return [0]*4\n",
    "        if any(isinf(t)): return [0]*4\n",
    "        acc = score_metric(bdy, np.round(t))\n",
    "        acc_real = acc#accuracy_score(bdy, np.round(t))\n",
    "\n",
    "        p = activate_graph(g, vstack([bdx_val.T, ones(bdx_val.shape[0])*bias_const]), \n",
    "                           num_outputs=num_outputs)[0]\n",
    "        t1 = ((np.tanh(p)+1.0)/2)#(p-np.min(p))/(np.max(p) - np.min(p))\n",
    "        if any(isnan(t1)): return [0]*4\n",
    "        if any(isinf(t1)): return [0]*4\n",
    "        acc1 = score_metric(bdy_val, np.round(t1))\n",
    "        acc1_real = acc1#accuracy_score(bdy_val, np.round(t1))\n",
    "    \n",
    "        return float(acc), float(acc1), float(acc_real), float(acc1_real)\n",
    "#     except Exception as ex:\n",
    "#         print(ex)\n",
    "#         return [0]*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "m = zeros((max_nodes,max_nodes))\n",
    "m[0:num_inputs, num_inputs:num_inputs+num_outputs] = 1\n",
    "g = nx.DiGraph(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def make_pos(m, wm):\n",
    "    inpout_list = list(range(num_inputs))+list(range(num_inputs+num_outputs))\n",
    "    g = nx.DiGraph(m)\n",
    "\n",
    "    for i in range(max_nodes):\n",
    "        for j in range(max_nodes):\n",
    "            if m[i,j] != 0:\n",
    "                g.edges[i,j]['weight'] = wm[i,j]\n",
    "        \n",
    "    pos = {}#nx.spring_layout(g,k=0.9)\n",
    "    #for k,v in pos.items():\n",
    "    #    pos[k] *= 0.6\n",
    "    #    pos[k] -= 0.1\n",
    "    # will provide my own coords\n",
    "    for i,x in zip(range(num_inputs), linspace(0,1,num_inputs)):\n",
    "        pos[i] = array([x,1])\n",
    "    \n",
    "    if num_outputs == 1:\n",
    "        pos[num_inputs] = array([0.5,0])\n",
    "    elif num_outputs == 2:\n",
    "        pos[num_inputs] = array([0.25,0])\n",
    "        pos[num_inputs+1] = array([0.75,0])\n",
    "    else:\n",
    "        for i,x in zip(range(num_inputs,num_inputs+num_outputs), \n",
    "                       linspace(0,1,num_outputs)):\n",
    "            pos[i] = array([x,0])\n",
    "    \n",
    "    for i,x in zip(range(num_inputs+num_outputs,max_nodes), \n",
    "                   linspace(0,1,max_nodes-(num_inputs+num_outputs))):\n",
    "        if i%2==0: \n",
    "            ofs = 0.07\n",
    "        else:\n",
    "            ofs = -0.15\n",
    "        pos[i] = array([x,0.5+ofs])\n",
    "    return pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pos = make_pos(m, wm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### probs and params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "env_type = 'discrete' # or discrete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "add_node_prob = 1.0\n",
    "add_link_prob = 1.0\n",
    "rem_node_prob = 1.0\n",
    "rem_link_prob = 1.0\n",
    "\n",
    "mut_weight_prob = 1.0\n",
    "replace_weight_prob = 1.0\n",
    "defaultize_weight_prob = 1.0\n",
    "\n",
    "min_mut_power = 0.05\n",
    "max_mut_power = 0.75\n",
    "\n",
    "min_extra_links = 2\n",
    "min_extra_links_enabled = 1\n",
    "\n",
    "max_steps = 5000\n",
    "max_stag = 500\n",
    "\n",
    "steps_to_charge_newnode = 10\n",
    "steps_to_charge_newlink = 10\n",
    "\n",
    "display_every = 1000\n",
    "display_matrix = 0\n",
    "display_weight_matrix = 0\n",
    "display_sleep = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "penalize_unsuccessful_move = 0\n",
    "penalize_unsuccessful_move_mul = 0.9\n",
    "\n",
    "penalize_unsuccessful_modify = 0\n",
    "penalize_unsuccessful_modify_mul = 0.5\n",
    "\n",
    "penalize_unsuccessful_struct = 0\n",
    "penalize_unsuccessful_struct_mul = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# caret movement will wrap around, it can never be unsuccessful\n",
    "infinite_caret = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# implemented are: \n",
    "# sparse_diff_best - given when it beats best record, 0 otherwise\n",
    "# sparse_diff_prev - given when it beats previous score, 0 otherwise\n",
    "# dense_abs - always given the current score\n",
    "# dense_diff_best - always given the (current score - best)\n",
    "# dense_diff_prev - always given the (current score - previous)\n",
    "# dense_diff_mean_prev - always given the (current score - mean of previous N scores)\n",
    "reward_type = 'dense_abs' \n",
    "\n",
    "last_scores_window = 10 # parameter for dense_diff_mean_prev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class GraphMakerEnv(gym.Env):\n",
    "\n",
    "    metadata = {'render.modes': ['human']}\n",
    "    \n",
    "    def _configure(self, display=None):\n",
    "        self.display = display\n",
    "\n",
    "    def _seed(self, seed=None):\n",
    "        self.np_random, seed = seeding.np_random(seed)\n",
    "        return [seed]\n",
    "\n",
    "    def _step(self, action):\n",
    "        return self.step(action)\n",
    "\n",
    "    def _reset(self):\n",
    "        return self.reset()\n",
    "    \n",
    "    def _render(self, mode='human', close=False):\n",
    "        # ... TODO\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def __init__(self, max_nodes, max_links, num_inputs, num_outputs):\n",
    "\n",
    "    self.max_nodes = max_nodes\n",
    "    self.max_links = max_links\n",
    "    self.num_inputs = num_inputs\n",
    "    self.num_outputs = num_outputs\n",
    "\n",
    "    if env_type == 'box':\n",
    "        self.action_space = gym.spaces.Box(-1*np.ones(5), np.ones(5))\n",
    "    else:\n",
    "        self.action_space = gym.spaces.Discrete(14)\n",
    "\n",
    "    self.matrix = zeros((self.max_nodes, self.max_nodes))\n",
    "    self.weight_matrix = zeros((self.max_nodes, self.max_nodes))\n",
    "    self.node_matrix = zeros((self.max_nodes-self.num_inputs, len(funcs)))\n",
    "#     self.matrix[0:self.num_inputs, \n",
    "#                 self.num_inputs:self.num_inputs+self.num_outputs] = 1\n",
    "#     for i in range(self.max_nodes):\n",
    "#         for j in range(self.max_nodes):\n",
    "#             if self.matrix[i,j]==1:\n",
    "#                 self.weight_matrix[i,j] = 1.0\n",
    "#     for i in range(self.num_inputs, num_nodes(self.matrix)):\n",
    "#         self.node_matrix[i-self.num_inputs, rnd.choice(arange(len(funcs)))]=1\n",
    "\n",
    "    self.caret_row = 0\n",
    "    self.caret_col = self.num_inputs\n",
    "    self.node_caret = 0\n",
    "    exmpi = make_input(self.matrix, self.weight_matrix, self.node_matrix, \n",
    "                              self.caret_row, self.caret_col, self.node_caret,\n",
    "                              self.max_nodes, self.max_links, \n",
    "                              self.num_inputs, self.num_outputs)\n",
    "\n",
    "    self.observation_space = gym.spaces.Box(np.zeros(exmpi.shape[0]),\n",
    "                                        np.ones(exmpi.shape[0]), )\n",
    "    self.best_ever = (0, 0)\n",
    "    self.prev_score = [(0, 0, 0, 0)]\n",
    "    self.reset()\n",
    "\n",
    "GraphMakerEnv.__init__ = __init__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### move"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def move(self, action):\n",
    "\n",
    "    \n",
    "    move_attempt = 0\n",
    "    move_success = 0\n",
    "    modify_attempt = 0\n",
    "    modify_success = 0\n",
    "    struct_attempt = 0\n",
    "    struct_success = 0\n",
    "    \n",
    "    wants_test = False\n",
    "\n",
    "\n",
    "    # execute action\n",
    "    if action == 0: \n",
    "        # move caret up\n",
    "        if not infinite_caret:\n",
    "            move_attempt = 1\n",
    "            if self.caret_row > 0:\n",
    "                self.caret_row -= 1\n",
    "                move_success = 1\n",
    "            else:\n",
    "                move_success = 0\n",
    "        else:\n",
    "            move_attempt = 1\n",
    "            move_success = 1\n",
    "            self.caret_row -= 1\n",
    "            if self.caret_row <= -1: \n",
    "                self.caret_row = num_nodes(self.matrix)-1\n",
    "            \n",
    "\n",
    "    elif action == 1:\n",
    "        # move caret down\n",
    "        if not infinite_caret:\n",
    "            move_attempt = 1\n",
    "            if self.caret_row < num_nodes(self.matrix)-1:\n",
    "                self.caret_row += 1\n",
    "                move_success = 1\n",
    "            else:\n",
    "                move_success = 0\n",
    "        else:\n",
    "            move_attempt = 1\n",
    "            move_success = 1\n",
    "            self.caret_row += 1\n",
    "            if self.caret_row >= num_nodes(self.matrix): \n",
    "                self.caret_row = 0\n",
    "\n",
    "    elif action == 2:\n",
    "        # move caret left\n",
    "        if not infinite_caret:\n",
    "            move_attempt = 1\n",
    "            if self.caret_col > self.num_inputs:\n",
    "                self.caret_col -= 1\n",
    "                move_success = 1\n",
    "            else:\n",
    "                move_success = 0\n",
    "        else:\n",
    "            move_attempt = 1\n",
    "            move_success = 1\n",
    "            self.caret_col -= 1\n",
    "            if self.caret_col <= self.num_inputs-1: \n",
    "                self.caret_col = num_nodes(self.matrix)-1\n",
    "\n",
    "    elif action == 3:\n",
    "        # move caret right\n",
    "        if not infinite_caret:\n",
    "            move_attempt = 1\n",
    "            if self.caret_col < num_nodes(self.matrix)-1:\n",
    "                self.caret_col += 1\n",
    "                move_success = 1\n",
    "            else:\n",
    "                move_success = 0\n",
    "        else:\n",
    "            move_attempt = 1\n",
    "            move_success = 1\n",
    "            self.caret_col += 1\n",
    "            if self.caret_col >= num_nodes(self.matrix): \n",
    "                self.caret_col = self.num_inputs\n",
    "\n",
    "#     elif action == 4:\n",
    "#         # randomize caret\n",
    "#         move_attempt = 1\n",
    "#         prv = (self.caret_row, self.caret_col)\n",
    "#         self.caret_row = rnd.randint(0, num_nodes(self.matrix)-1)\n",
    "#         self.caret_col = rnd.randint(self.num_inputs, num_nodes(self.matrix)-1)\n",
    "#         if (self.caret_row, self.caret_col) != prv:\n",
    "#             move_success = 1\n",
    "#         else:\n",
    "#             move_success = 0\n",
    "\n",
    "    elif action == 4:\n",
    "        # move node caret up\n",
    "        if not infinite_caret:\n",
    "            move_attempt = 1\n",
    "            if self.node_caret > 0:\n",
    "                self.node_caret -= 1\n",
    "                move_success = 1\n",
    "            else:\n",
    "                move_success = 0\n",
    "        else:\n",
    "            move_attempt = 1\n",
    "            move_success = 1\n",
    "            self.node_caret -= 1\n",
    "            if self.node_caret <= -1:\n",
    "                self.node_caret = num_nodes(self.matrix)-self.num_inputs-1\n",
    "\n",
    "    elif action == 5:\n",
    "        # move node caret down\n",
    "        if not infinite_caret:\n",
    "            move_attempt = 1\n",
    "            if self.node_caret < num_nodes(self.matrix)-self.num_inputs-1:\n",
    "                self.node_caret += 1\n",
    "                move_success = 1\n",
    "            else:\n",
    "                move_success = 0\n",
    "        else:\n",
    "            move_attempt = 1\n",
    "            move_success = 1\n",
    "            self.node_caret += 1\n",
    "            if self.node_caret >= num_nodes(self.matrix)-self.num_inputs:\n",
    "                self.node_caret = 0\n",
    "\n",
    "#     elif action == 7:\n",
    "#         # randomize node caret\n",
    "#         move_attempt = 1\n",
    "#         prv = self.node_caret\n",
    "#         self.node_caret = rnd.randint(0, num_nodes(self.matrix)-self.num_inputs-1)\n",
    "#         if self.node_caret != prv:\n",
    "#             move_success = 1\n",
    "#         else:\n",
    "#             move_success = 0\n",
    "\n",
    "    elif action == 6:\n",
    "        # add link attempt\n",
    "        struct_attempt = 1\n",
    "        if (rnd.uniform(0,1)<add_link_prob) and (self.newlink_charge <= 0) and add_link(self.matrix, self.caret_row, self.caret_col, \n",
    "                                                                                        self.num_inputs, self.num_outputs, self.max_links, \n",
    "                                                                                        wm = self.weight_matrix):\n",
    "            struct_success = 1\n",
    "            self.newlink_charge = steps_to_charge_newlink\n",
    "        else:\n",
    "            struct_success = 0\n",
    "\n",
    "    elif action == 7:\n",
    "        # remove link attempt\n",
    "        struct_attempt = 1\n",
    "        if (rnd.uniform(0,1)<rem_link_prob) and remove_link(self.matrix, self.caret_row, self.caret_col, \n",
    "                                                            self.num_inputs, self.num_outputs, \n",
    "                                                            wm = self.weight_matrix):\n",
    "            struct_success = 1\n",
    "            # also boost charge by 50%\n",
    "            self.newlink_charge *= 0.5\n",
    "        else:\n",
    "            struct_success = 0\n",
    "\n",
    "    elif action == 8:\n",
    "        # add node attempt\n",
    "        struct_attempt = 1\n",
    "        if (rnd.uniform(0,1)<add_node_prob) and (self.newnode_charge <= 0) and add_node(self.matrix, self.caret_row, self.caret_col, \n",
    "                                                                                        self.num_inputs, self.num_outputs, self.max_links, \n",
    "                                                                                        wm = self.weight_matrix, \n",
    "                                                                                        nm = self.node_matrix):\n",
    "            struct_success = 1\n",
    "            self.newnode_charge = steps_to_charge_newnode\n",
    "        else:\n",
    "            struct_success = 0      \n",
    "\n",
    "    elif action == 9:\n",
    "        # remove node attempt\n",
    "        struct_attempt = 1\n",
    "        if (rnd.uniform(0,1)<rem_link_prob) and remove_node(self.matrix, self.caret_row, \n",
    "                                                            self.num_inputs, self.num_outputs, \n",
    "                                                            wm = self.weight_matrix, \n",
    "                                                            nm = self.node_matrix):\n",
    "            struct_success = 1\n",
    "            # also boost charge by 50%\n",
    "            self.newnode_charge *= 0.5\n",
    "        else:\n",
    "            struct_success = 0\n",
    "\n",
    "    elif action == 10:\n",
    "        # mutate parameter +\n",
    "        modify_attempt = 1\n",
    "        if (rnd.uniform(0,1)<mut_weight_prob) and (self.matrix[self.caret_row,\n",
    "                                                               self.caret_col] == 1):\n",
    "            self.weight_matrix[self.caret_row, self.caret_col] += \\\n",
    "                            rnd.uniform(min_mut_power, max_mut_power)\n",
    "            modify_success = 1\n",
    "        else:\n",
    "            modify_success = 0\n",
    "\n",
    "    elif action == 11:\n",
    "        # mutate parameter -\n",
    "        modify_attempt = 1\n",
    "        if (rnd.uniform(0,1)<mut_weight_prob) and (self.matrix[self.caret_row, \n",
    "                                                               self.caret_col] == 1):\n",
    "            self.weight_matrix[self.caret_row, self.caret_col] -= \\\n",
    "                            rnd.uniform(min_mut_power, max_mut_power)\n",
    "            modify_success = 1\n",
    "        else:\n",
    "            modify_success = 0\n",
    "\n",
    "#     elif action == 14:\n",
    "#         # randomize parameter \n",
    "#         modify_attempt = 1\n",
    "#         if (rnd.uniform(0,1)<replace_weight_prob) and (self.matrix[self.caret_row, \n",
    "#                                                                    self.caret_col] == 1):\n",
    "#             self.weight_matrix[self.caret_row, self.caret_col] = \\\n",
    "#                             rnd.uniform(-max_init_weight, max_init_weight)\n",
    "#             modify_success = 1\n",
    "#         else:\n",
    "#             modify_success = 0\n",
    "\n",
    "#     elif action == 12:\n",
    "#         # set parameter to 1\n",
    "#         modify_attempt = 1\n",
    "#         if (rnd.uniform(0,1)<defaultize_weight_prob) and (self.matrix[self.caret_row, \n",
    "#                                                                       self.caret_col] == 1):\n",
    "#             self.weight_matrix[self.caret_row, self.caret_col] = init_structural_with\n",
    "#             modify_success = 1\n",
    "#         else:\n",
    "#             modify_success = 0\n",
    "\n",
    "    elif action == 12:\n",
    "        # mutate node parameter +\n",
    "        modify_attempt = 1\n",
    "        if np.sum(self.node_matrix[self.node_caret, :])==1:\n",
    "            ocp = argmax(self.node_matrix[self.node_caret, :])\n",
    "            ncp = ocp+1\n",
    "            cp = np.clip(ncp, 0, len(funcs)-1)\n",
    "            self.node_matrix[self.node_caret, :] = 0\n",
    "            self.node_matrix[self.node_caret, cp] = 1\n",
    "            if cp != ocp:\n",
    "                modify_success = 1\n",
    "            else:\n",
    "                modify_success = 0\n",
    "        else:\n",
    "            modify_success = 0\n",
    "\n",
    "    elif action == 13:\n",
    "        # mutate node parameter -\n",
    "        modify_attempt = 1\n",
    "        if np.sum(self.node_matrix[self.node_caret, :])==1:\n",
    "            ocp = argmax(self.node_matrix[self.node_caret, :])\n",
    "            ncp = ocp-1\n",
    "            cp = np.clip(ncp, 0, len(funcs)-1)\n",
    "            self.node_matrix[self.node_caret, :] = 0\n",
    "            self.node_matrix[self.node_caret, cp] = 1\n",
    "            if cp != ocp:\n",
    "                modify_success = 1\n",
    "            else:\n",
    "                modify_success = 0\n",
    "        else:\n",
    "            modify_success = 0\n",
    "\n",
    "#     elif action == 18:\n",
    "#         # randomize node parameter \n",
    "#         modify_attempt = 1\n",
    "#         if np.sum(self.node_matrix[self.node_caret, :])==1:\n",
    "#             ocp = argmax(self.node_matrix[self.node_caret, :])\n",
    "#             ncp = rnd.choice(arange(len(funcs)))\n",
    "#             self.node_matrix[self.node_caret, :] = 0\n",
    "#             self.node_matrix[self.node_caret, ncp] = 1\n",
    "#             if ocp != ncp:\n",
    "#                 modify_success = 1\n",
    "#             else:\n",
    "#                 modify_success = 0\n",
    "#         else:\n",
    "#             modify_success = 0\n",
    "\n",
    "#     elif action == 15:\n",
    "#         # set node parameter to default\n",
    "#         modify_attempt = 1\n",
    "#         if np.sum(self.node_matrix[self.node_caret, :])==1:\n",
    "#             ocp = argmax(self.node_matrix[self.node_caret, :])\n",
    "#             ncp = 0\n",
    "#             self.node_matrix[self.node_caret, :] = 0\n",
    "#             self.node_matrix[self.node_caret, ncp] = 1\n",
    "#             if ocp != ncp:\n",
    "#                 modify_success = 1\n",
    "#             else:\n",
    "#                 modify_success = 0\n",
    "#         else:\n",
    "#             modify_success = 0\n",
    "\n",
    "#         elif action == 14:\n",
    "#             # save state\n",
    "#             self.saved_matrix = np.copy(self.matrix)\n",
    "#             self.saved_weight_matrix = np.copy(self.weight_matrix)\n",
    "#             self.saved_caret = (self.caret_row, self.caret_col)\n",
    "#             success = 1\n",
    "#             modified = False\n",
    "\n",
    "#         elif action == 15:\n",
    "#             # restore state\n",
    "#             self.matrix = np.copy(self.saved_matrix)\n",
    "#             self.weight_matrix = np.copy(self.saved_weight_matrix)\n",
    "#             self.caret_row, self.caret_col = self.saved_caret\n",
    "#             success = 1\n",
    "#             modified = True\n",
    "\n",
    "#     elif action == 14:\n",
    "#         # request testing\n",
    "#         wants_test = True\n",
    "    return move_attempt,move_success, modify_attempt,modify_success, \\\n",
    "    struct_attempt,struct_success, wants_test\n",
    "\n",
    "\n",
    "GraphMakerEnv.move = move"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### move_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def move_box(self, actions):\n",
    "    \n",
    "    # interpret all actions from the start\n",
    "    caret_speed_row = 0\n",
    "    if actions[0] < -0.33: caret_speed_row = -1\n",
    "    if actions[0] > 0.33: caret_speed_row = 1\n",
    "\n",
    "    caret_speed_col = 0\n",
    "    if actions[1] < -0.33: caret_speed_col = -1\n",
    "    if actions[1] > 0.33: caret_speed_col = 1\n",
    "        \n",
    "    node_caret_speed = 0\n",
    "    if actions[2] < -0.33: node_caret_speed = -1\n",
    "    if actions[2] > 0.33: node_caret_speed = 1\n",
    "        \n",
    "#     link_decision = 0\n",
    "#     if actions[3] < -0.33: link_decision = -1\n",
    "#     if actions[3] > 0.33: link_decision = 1\n",
    "\n",
    "#     node_decision = 0\n",
    "#     if actions[4] < -0.33: node_decision = -1\n",
    "#     if actions[4] > 0.33: node_decision = 1\n",
    "    \n",
    "    new_weight = actions[3]*max_weight\n",
    "    new_func = clip(int(((actions[4]+1)/2)*(len(funcs)-1)), \n",
    "                    0, len(funcs)-1)\n",
    "        \n",
    "    # execute actions\n",
    "\n",
    "    # move carets\n",
    "    self.caret_row += caret_speed_row\n",
    "    self.caret_col += caret_speed_col\n",
    "    self.node_caret += node_caret_speed\n",
    "    \n",
    "    if not infinite_caret:\n",
    "        self.caret_row = clip(self.caret_row, 0, num_nodes(self.matrix)-1)\n",
    "        self.caret_col = clip(self.caret_col, \n",
    "                              self.num_inputs, num_nodes(self.matrix)-1)\n",
    "        self.node_caret = clip(self.node_caret, 0, num_nodes(self.matrix)-1)\n",
    "    else:\n",
    "        if self.caret_row < 0: self.caret_row = (num_nodes(self.matrix)-1)\n",
    "        if self.caret_row > (num_nodes(self.matrix)-1): self.caret_row = 0\n",
    "        if self.caret_col < self.num_inputs: self.caret_col = (num_nodes(self.matrix)-1)\n",
    "        if self.caret_col > (num_nodes(self.matrix)-1): self.caret_col = self.num_inputs\n",
    "        if self.node_caret < 0: self.node_caret = num_nodes(self.matrix)\n",
    "        if self.node_caret > num_nodes(self.matrix): self.node_caret = 0\n",
    "            \n",
    "    # execute link decision\n",
    "#     if link_decision == -1:\n",
    "#         if (rnd.uniform(0,1)<rem_link_prob) and \\\n",
    "#             remove_link(self.matrix, self.caret_row, self.caret_col, \n",
    "#                         self.num_inputs, self.num_outputs, \n",
    "#                         wm = self.weight_matrix):\n",
    "#                     # also boost charge by 50%\n",
    "#                     self.newlink_charge *= 0.5\n",
    "#     elif link_decision == 1:\n",
    "#         if (rnd.uniform(0,1)<add_link_prob) and \\\n",
    "#             (self.newlink_charge <= 0) and \\\n",
    "#             add_link(self.matrix, self.caret_row, self.caret_col, \n",
    "#                     self.num_inputs, self.num_outputs, self.max_links, \n",
    "#                     wm = self.weight_matrix):\n",
    "#             self.newlink_charge = steps_to_charge_newlink\n",
    "            \n",
    "#     # execute node decision\n",
    "#     if node_decision == -1:\n",
    "#         if (rnd.uniform(0,1)<rem_link_prob) and \\\n",
    "#             remove_node(self.matrix, self.caret_row, \n",
    "#                         self.num_inputs, self.num_outputs, \n",
    "#                         wm = self.weight_matrix, \n",
    "#                         nm = self.node_matrix):\n",
    "#             # also boost charge by 50%\n",
    "#             self.newnode_charge *= 0.5\n",
    "#     elif node_decision == 1:\n",
    "#         if (rnd.uniform(0,1)<add_node_prob) and (self.newnode_charge <= 0) and \\\n",
    "#             add_node(self.matrix, self.caret_row, self.caret_col, \n",
    "#                      self.num_inputs, self.num_outputs, self.max_links, \n",
    "#                      wm = self.weight_matrix, \n",
    "#                      nm = self.node_matrix):\n",
    "#                 self.newnode_charge = steps_to_charge_newnode\n",
    "            \n",
    "    # set the new parameters is possible\n",
    "    # new weight\n",
    "    if (rnd.uniform(0,1)<mut_weight_prob) and \\\n",
    "        (self.matrix[self.caret_row,\n",
    "                     self.caret_col] == 1):\n",
    "        self.weight_matrix[self.caret_row, self.caret_col] = new_weight\n",
    "    # new func\n",
    "#     try:\n",
    "#         if np.sum(self.node_matrix[self.node_caret, :])==1:\n",
    "#             self.node_matrix[self.node_caret, :] = 0\n",
    "#             self.node_matrix[self.node_caret, new_func] = 1\n",
    "#     except IndexError:\n",
    "#         pass\n",
    "        \n",
    "    self.caret_row = int(self.caret_row)\n",
    "    self.caret_col = int(self.caret_col)\n",
    "    self.node_caret = int(self.node_caret)\n",
    "\n",
    "\n",
    "GraphMakerEnv.move_box = move_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#%debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def step(self, action):\n",
    "\n",
    "    self.step_counter += 1\n",
    "\n",
    "    # for testing\n",
    "    #action = rnd.choice(arange(15))\n",
    "    if env_type == 'box':\n",
    "        move_attempt,move_success, modify_attempt,modify_success, \\\n",
    "        struct_attempt,struct_success,wants_test = 1,1,1,1,1,1,1\n",
    "        self.move_box(action)\n",
    "    else:\n",
    "        move_attempt,move_success, modify_attempt,modify_success, \\\n",
    "        struct_attempt,struct_success,wants_test = self.move(action)\n",
    "\n",
    "    self.newnode_charge -= 1\n",
    "    self.newlink_charge -= 1\n",
    "\n",
    "    self.caret_row = clip(self.caret_row, 0, num_nodes(self.matrix)-1)\n",
    "    self.caret_col = clip(self.caret_col, self.num_inputs, num_nodes(self.matrix)-1)\n",
    "    self.node_caret = clip(self.node_caret, 0, num_nodes(self.matrix)-num_inputs-1)\n",
    "    self.weight_matrix = clip(self.weight_matrix, -max_weight, max_weight)\n",
    "\n",
    "    self.newnode_charge = clip(self.newnode_charge, 0, steps_to_charge_newnode)\n",
    "    self.newlink_charge = clip(self.newlink_charge, 0, steps_to_charge_newlink)\n",
    "\n",
    "\n",
    "    info = {}\n",
    "    done = False\n",
    "\n",
    "    # reevaluate if modified\n",
    "    if struct_success or modify_success:\n",
    "        f,fr,facc,faccr = test_xor(self.matrix, self.weight_matrix, self.node_matrix, \n",
    "                     self.num_inputs, self.num_outputs, self.max_nodes)\n",
    "    else:\n",
    "        f,fr,facc,faccr = self.prev_score[-1]\n",
    "        \n",
    "        \n",
    "\n",
    "\n",
    "    reward = 0\n",
    "    if reward_type == 'sparse_diff_best':\n",
    "        if (f - self.best[0]) > 0.0:\n",
    "            reward = (f-self.best[0])*f\n",
    "        else:\n",
    "            reward = 0\n",
    "    elif reward_type == 'sparse_diff_prev':\n",
    "        if (f - self.prev_score[-1][0]) > 0.0:\n",
    "            reward = (f-self.prev_score[-1][0])*f\n",
    "        else:\n",
    "            reward = 0\n",
    "        \n",
    "    elif reward_type == 'dense_abs':\n",
    "        reward = f \n",
    "    elif reward_type == 'dense_diff_best':\n",
    "        reward = f - self.best\n",
    "    elif reward_type == 'dense_diff_prev':\n",
    "        reward = f - self.prev_score[-1][0]\n",
    "    elif reward_type == 'dense_diff_mean_prev':\n",
    "        scs = mean([x[0] for x in self.prev_score[-last_scores_window:]])\n",
    "        reward = f - scs\n",
    "\n",
    "\n",
    "    # Soft constraints applied to the reward\n",
    "    if prefer_incoming_links_enabled:\n",
    "        g = nx.DiGraph(self.matrix)\n",
    "        g.remove_nodes_from(list(nx.isolates(g)))\n",
    "        # make sure the remaining nodes will be connected \n",
    "        inps=list(range(self.num_inputs)) \n",
    "        #outs=list(range(self.num_inputs, self.num_inputs+self.num_outputs))\n",
    "        for k in g.nodes():\n",
    "            if (k not in inps):# and (k not in outs):\n",
    "                # must be hidden\n",
    "                if (len(list(g.in_edges(nbunch=k))) != prefer_incoming_links ):\n",
    "                    reward *= 0.2\n",
    "\n",
    "\n",
    "    if min_extra_links_enabled:\n",
    "        if enable_disconnected_inputs:\n",
    "            if np.sum(self.matrix) < 1+min_extra_links:\n",
    "                reward = 0 # force at least one hidden node\n",
    "                f,fr = 0,0\n",
    "        else:\n",
    "            if np.sum(self.matrix) < self.num_inputs+min_extra_links:\n",
    "                reward = 0 # force at least one hidden node\n",
    "                f,fr = 0,0\n",
    "\n",
    "    if penalize_unsuccessful_move:\n",
    "        if move_attempt and (not move_success):\n",
    "            reward *= penalize_unsuccessful_move_mul\n",
    "    if penalize_unsuccessful_modify:\n",
    "        if modify_attempt and (not modify_success):\n",
    "            reward *= penalize_unsuccessful_modify_mul\n",
    "    if penalize_unsuccessful_struct:\n",
    "        if struct_attempt and (not struct_success):\n",
    "            reward *= penalize_unsuccessful_struct_mul\n",
    "\n",
    "    #reward = np.sum(self.matrix) - prev#rnd.uniform(-1,1)\n",
    "    #af = test_xor(self.matrix, self.weight_matrix, \n",
    "    #                 self.num_inputs, self.num_outputs, self.max_nodes)\n",
    "    \n",
    "\n",
    "    if (f>self.best[0]) or (self.step_counter % display_every == 0):\n",
    "        clear_output(wait=True)\n",
    "        see_graph(self.matrix, pos=pos, \n",
    "                  wm=self.weight_matrix, nm=self.node_matrix)\n",
    "        plt.show()\n",
    "        print(\"%d steps, %3.4f / %3.4f, best: %3.4f / %3.4f\\nbest ever: %3.4f / %3.4f\" % \n",
    "              (self.step_counter, f, fr, \n",
    "               self.best[0], self.best[1], \n",
    "               self.best_ever[0], self.best_ever[1]))\n",
    "        print('Accuracy scores: %3.4f / %3.4f' % (facc, faccr))\n",
    "        if env_type != 'box':\n",
    "            print('Action: %s\\nReward: %3.5f, charge for n/l: %3.4f/%3.4f' \n",
    "                  % (action_codes[action], reward, 1.0-self.newnode_charge/steps_to_charge_newnode, \n",
    "                     1.0-self.newlink_charge/steps_to_charge_newlink))\n",
    "        else:\n",
    "            print('Reward: %3.5f, charge for n/l: %3.4f/%3.4f' \n",
    "                  % (reward, 1.0-self.newnode_charge/steps_to_charge_newnode, \n",
    "                     1.0-self.newlink_charge/steps_to_charge_newlink))\n",
    "        print('Caret row/col: %d/%d' % (self.caret_row, self.caret_col))\n",
    "\n",
    "        if display_matrix:\n",
    "            sm = np.copy(self.matrix)\n",
    "            sm[self.caret_row, self.caret_col] = 666\n",
    "            print(sm[0:num_nodes(self.matrix), \n",
    "                     self.num_inputs:num_nodes(self.matrix)])\n",
    "            #print(sm)\n",
    "        if display_weight_matrix:\n",
    "            print(self.weight_matrix[0:num_nodes(self.matrix), \n",
    "                     self.num_inputs:num_nodes(self.matrix)])\n",
    "            #print(self.weight_matrix)\n",
    "\n",
    "        if display_sleep > 0:\n",
    "            time.sleep(display_sleep)\n",
    "            \n",
    "    if f > self.best[0]:\n",
    "        self.best = f,fr\n",
    "        self.stag = 0\n",
    "    else:\n",
    "        self.stag += 1\n",
    "\n",
    "    if f > self.best_ever[0]:\n",
    "        self.best_ever = f,fr            \n",
    "\n",
    "    if (self.step_counter > max_steps) or (self.stag > max_stag):\n",
    "        done = True\n",
    "            \n",
    "\n",
    "\n",
    "    observation = make_input(self.matrix, self.weight_matrix, self.node_matrix,\n",
    "                             self.caret_row, self.caret_col, self.node_caret,\n",
    "                             self.max_nodes, self.max_links, \n",
    "                             self.num_inputs, self.num_outputs, \n",
    "                             la=float(move_success or modify_success or struct_success),\n",
    "                             ls=f, \n",
    "                             nnc=1.0-self.newnode_charge/steps_to_charge_newnode,\n",
    "                             nlc=1.0-self.newlink_charge/steps_to_charge_newlink, )\n",
    "\n",
    "    self.prev_score.append( (f,fr,facc,faccr) )\n",
    "    self.prev_reward.append( reward )\n",
    "\n",
    "    return observation, reward, done, info\n",
    "\n",
    "GraphMakerEnv.step = step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def reset(self):\n",
    "\n",
    "    print('Env reset')\n",
    "    self.step_counter = 0\n",
    "    self.stag = 0\n",
    "\n",
    "    self.matrix = zeros((self.max_nodes, self.max_nodes))\n",
    "    self.weight_matrix = zeros((self.max_nodes, self.max_nodes))\n",
    "    self.node_matrix = zeros((self.max_nodes-self.num_inputs, len(funcs)))\n",
    "    if enable_disconnected_inputs:\n",
    "        cn = rnd.randint(0,self.num_inputs-1)\n",
    "        self.matrix[cn, \n",
    "                    self.num_inputs:self.num_inputs+self.num_outputs] = 1\n",
    "        if enforce_bias_link:\n",
    "            self.matrix[self.num_inputs-1, \n",
    "                        self.num_inputs:self.num_inputs+self.num_outputs] = 1 # bias\n",
    "    else:\n",
    "#         self.matrix[0:self.num_inputs, \n",
    "#                     self.num_inputs:self.num_inputs+self.num_outputs] = 1\n",
    "        \n",
    "        # connect every input to every hidden \n",
    "        nh = self.max_nodes - (self.num_inputs+self.num_outputs)\n",
    "        for h in range(self.num_inputs+self.num_outputs, self.max_nodes):\n",
    "            for i in range(self.num_inputs):\n",
    "                #add_link(self.matrix, i, h, \n",
    "                #         self.num_inputs, self.num_outputs, self.max_links, \n",
    "                #         wm=self.weight_matrix)\n",
    "                self.matrix[i,h]=1\n",
    "        # connect all hidden to outputs\n",
    "        for h in range(self.num_inputs+self.num_outputs, self.max_nodes):\n",
    "            for o in range(self.num_outputs):\n",
    "                #add_link(self.matrix, h, self.num_inputs+o, \n",
    "                #         self.num_inputs, self.num_outputs, self.max_links, \n",
    "                #         wm=self.weight_matrix)\n",
    "                self.matrix[h,o]=1\n",
    "            \n",
    "#        self.matrix[0:self.num_inputs, \n",
    "#                    self.num_inputs+self.num_outputs:self.max_nodes-self.num_inputs+self.num_outputs-1] = 1\n",
    "\n",
    "    for i in range(self.max_nodes):\n",
    "        for j in range(self.max_nodes):\n",
    "            if self.matrix[i,j]==1:\n",
    "                if random_init_structural:\n",
    "                    self.weight_matrix[i,j] = rnd.uniform(-max_init_weight,max_init_weight)\n",
    "                else:\n",
    "                    self.weight_matrix[i,j] = init_structural_with\n",
    "    for i in range(self.num_inputs, num_nodes(self.matrix)):\n",
    "        self.node_matrix[i-self.num_inputs, rnd.choice(arange(len(funcs)))]=1                    \n",
    "\n",
    "    f,fr, facc, faccr = test_xor(self.matrix, self.weight_matrix, self.node_matrix,\n",
    "                     self.num_inputs, self.num_outputs, self.max_nodes)\n",
    "\n",
    "    self.caret_row = rnd.randint(0, self.num_inputs-1)\n",
    "    self.caret_col = self.num_inputs\n",
    "    self.node_caret = 0\n",
    "\n",
    "    # these increase and when threshold is hit, then it can add new links/nodes\n",
    "    self.newnode_charge = 0\n",
    "    self.newlink_charge = 0\n",
    "\n",
    "    self.prev_score = [(f,fr,facc, faccr)]\n",
    "    self.best = f,fr\n",
    "\n",
    "    self.prev_reward = [0]\n",
    "\n",
    "    self.saved_matrix = np.copy(self.matrix)\n",
    "    self.saved_weight_matrix = np.copy(self.weight_matrix)\n",
    "    self.saved_caret = (self.caret_row, self.caret_col)\n",
    "\n",
    "    observation = make_input(self.matrix, self.weight_matrix, self.node_matrix,\n",
    "                             self.caret_row, self.caret_col, self.node_caret,\n",
    "                             self.max_nodes, self.max_links, \n",
    "                             self.num_inputs, self.num_outputs, \n",
    "                             la=1, ls=0, nnc=1.0, nlc=1.0)\n",
    "    return observation\n",
    "\n",
    "GraphMakerEnv.reset = reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#%debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Env reset\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "need at least one array to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-b9914e75cc43>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0menv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSubprocVecEnv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mGraphMakerEnv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_nodes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_links\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_cpu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0menv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGraphMakerEnv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_nodes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_links\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0menv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDummyVecEnv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-52-b7409394a289>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, max_nodes, max_links, num_inputs, num_outputs)\u001b[0m\n\u001b[0;32m     35\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_ever\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprev_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[0mGraphMakerEnv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-57-314600d846eb>\u001b[0m in \u001b[0;36mreset\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     f,fr, facc, faccr = test_xor(self.matrix, self.weight_matrix, self.node_matrix,\n\u001b[1;32m---> 51\u001b[1;33m                      self.num_inputs, self.num_outputs, self.max_nodes)\n\u001b[0m\u001b[0;32m     52\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcaret_row\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_inputs\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-42-7bd422d36985>\u001b[0m in \u001b[0;36mtest_xor\u001b[1;34m(m, wm, nm, num_inputs, num_outputs, max_nodes)\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m         p = activate_graph(g, vstack([bdx.T, ones(bdx.shape[0])*bias_const]), \n\u001b[1;32m---> 18\u001b[1;33m                            num_outputs=num_outputs)[0]\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m         \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtanh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#(p-np.min(p))/(np.max(p) - np.min(p))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-35-9fa7a7dbe932>\u001b[0m in \u001b[0;36mactivate_graph\u001b[1;34m(gr, inputs, num_outputs)\u001b[0m\n\u001b[0;32m     27\u001b[0m         \u001b[0mws\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'weight'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mts\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minedgw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         \u001b[1;31m# weighted stack\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m         \u001b[0minps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mws\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[0msact\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# this node's default activation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python36\\lib\\site-packages\\numpy\\core\\shape_base.py\u001b[0m in \u001b[0;36mvstack\u001b[1;34m(tup)\u001b[0m\n\u001b[0;32m    281\u001b[0m     \"\"\"\n\u001b[0;32m    282\u001b[0m     \u001b[0m_warn_for_nonsequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 283\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: need at least one array to concatenate"
     ]
    }
   ],
   "source": [
    "# multiprocess environment or not\n",
    "if 0:\n",
    "    n_cpu = 8\n",
    "    env = SubprocVecEnv([lambda: GraphMakerEnv(max_nodes, max_links, num_inputs, num_outputs) for i in range(n_cpu)])\n",
    "else:\n",
    "    env = GraphMakerEnv(max_nodes, max_links, num_inputs, num_outputs)\n",
    "    env = DummyVecEnv([lambda: env])\n",
    "\n",
    "t = 0\n",
    "\n",
    "# Custom MLP policy of two layers of size 32 each with tanh activation function\n",
    "policy_kwargs = dict(act_fun=tf.nn.tanh, net_arch=[#'lstm', \n",
    "                                                   128, #256, \n",
    "                                                   128, \n",
    "                                                  ])\n",
    "\n",
    "tblog = 'C:/Users/spook/Dropbox/Desktop/tblog'\n",
    "    \n",
    "[shutil.rmtree(tblog+'/'+x) for x in os.listdir(tblog) if x and ('_' in x)]\n",
    "time.sleep(10)\n",
    "\n",
    "\n",
    "# model = PPO2(MlpLstmPolicy, env, n_steps=30, \n",
    "#              verbose=0, nminibatches=1, \n",
    "#              #policy_kwargs=policy_kwargs, \n",
    "#              gamma=0.99,#0.99,\n",
    "#              ent_coef=0.01,#0.01,\n",
    "#              learning_rate=0.0005,\n",
    "#              vf_coef=0.5,\n",
    "#              max_grad_norm=0.5,\n",
    "#              lam=0.95,\n",
    "#              tensorboard_log=tblog)\n",
    "\n",
    "# model = ACKTR(MlpPolicy, env, verbose=0, #n_steps=5, \n",
    "# #              policy_kwargs=policy_kwargs, \n",
    "#               tensorboard_log='/home/peter/tblog')\n",
    "\n",
    "model = A2C(MlpPolicy, env, verbose=0, n_steps=64, \n",
    "#              policy_kwargs=policy_kwargs, \n",
    "              tensorboard_log=tblog)\n",
    "\n",
    "# model = TRPO(MlpPolicy, env, verbose=0,# n_steps=50, \n",
    "#               #policy_kwargs=policy_kwargs, \n",
    "#               tensorboard_log=tblog)\n",
    "\n",
    "# model = ACER(MlpPolicy, env, verbose=0,# n_steps=50, \n",
    "#               #policy_kwargs=policy_kwargs, \n",
    "#               tensorboard_log=tblog)\n",
    "\n",
    "\n",
    "#################################\n",
    "# Models for Box environments\n",
    "#################################\n",
    "# \n",
    "# model = SAC(SACMlpPolicy, env, verbose=0,# n_env=1,\n",
    "#                                          # n_steps=100, \n",
    "#                                          # n_batch=32\n",
    "#             gamma=0.995,\n",
    "#             tensorboard_log=tblog\n",
    "#            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "try:\n",
    "    model.learn(total_timesteps=5000000,\n",
    "                log_interval=10)\n",
    "except KeyboardInterrupt:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model.save('RL_model_1.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "m,wm = env.get_attr('matrix')[0], env.get_attr('weight_matrix')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "see_graph(m);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "np.sum(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# mytest = bdx_val\n",
    "# testPredictions = ogp.get_predictions(bdx_val, bdy_val, ensemble)\n",
    "# pdtest = pd.DataFrame({'PassengerId': bdx_test_df.PassengerId.astype(int),\n",
    "#                         'Survived': testPredictions.astype(int)})\n",
    "# pdtest.to_csv('titanic_gp.csv', index=False) # make submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "pycharm": {
     "name": "#%%\n"
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "name": "#%%\n"
    }
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "pycharm": {
     "name": "#%%\n"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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