framework for recurrect neural networks

FlucomaClients.cmake

@@ -142,19 +142,23 @@ add_client(Transients clients/rt/TransientClient.hpp CLASS RTTransientClient )
 
 #lib manipulation client group 
 add_client(DataSet clients/nrt/DataSetClient.hpp CLASS NRTThreadedDataSetClient GROUP MANIPULATION)
-add_client(DataSetQuery clients/nrt/DataSetQueryClient.hpp CLASS NRTThreadedDataSetQueryClient GROUP MANIPULATION)
+add_client(DataSeries clients/nrt/DataSeriesClient.hpp CLASS NRTThreadedDataSeriesClient GROUP MANIPULATION)
+# add_client(DataSetQuery clients/nrt/DataSetQueryClient.hpp CLASS NRTThreadedDataSetQueryClient GROUP MANIPULATION)
 add_client(LabelSet clients/nrt/LabelSetClient.hpp CLASS NRTThreadedLabelSetClient GROUP MANIPULATION)
-add_client(KDTree clients/nrt/KDTreeClient.hpp CLASS NRTThreadedKDTreeClient GROUP MANIPULATION)
-add_client(KMeans clients/nrt/KMeansClient.hpp CLASS NRTThreadedKMeansClient GROUP MANIPULATION)
-add_client(SKMeans clients/nrt/SKMeansClient.hpp CLASS NRTThreadedSKMeansClient GROUP MANIPULATION)
-add_client(KNNClassifier clients/nrt/KNNClassifierClient.hpp CLASS NRTThreadedKNNClassifierClient GROUP MANIPULATION)
-add_client(KNNRegressor clients/nrt/KNNRegressorClient.hpp CLASS NRTThreadedKNNRegressorClient GROUP MANIPULATION)
-add_client(Normalize clients/nrt/NormalizeClient.hpp CLASS NRTThreadedNormalizeClient GROUP MANIPULATION)
-add_client(RobustScale clients/nrt/RobustScaleClient.hpp CLASS NRTThreadedRobustScaleClient GROUP MANIPULATION)
-add_client(Standardize clients/nrt/StandardizeClient.hpp CLASS NRTThreadedStandardizeClient GROUP MANIPULATION)
-add_client(PCA clients/nrt/PCAClient.hpp CLASS NRTThreadedPCAClient GROUP MANIPULATION)
-add_client(MDS clients/nrt/MDSClient.hpp CLASS NRTThreadedMDSClient GROUP MANIPULATION)
-add_client(UMAP clients/nrt/UMAPClient.hpp CLASS NRTThreadedUMAPClient GROUP MANIPULATION)
-add_client(MLPRegressor clients/nrt/MLPRegressorClient.hpp CLASS NRTThreadedMLPRegressorClient GROUP MANIPULATION)
-add_client(MLPClassifier clients/nrt/MLPClassifierClient.hpp CLASS NRTThreadedMLPClassifierClient GROUP MANIPULATION)
-add_client(Grid clients/nrt/GridClient.hpp CLASS NRTThreadedGridClient GROUP MANIPULATION)
+# add_client(KDTree clients/nrt/KDTreeClient.hpp CLASS NRTThreadedKDTreeClient GROUP MANIPULATION)
+# add_client(KMeans clients/nrt/KMeansClient.hpp CLASS NRTThreadedKMeansClient GROUP MANIPULATION)
+# add_client(SKMeans clients/nrt/SKMeansClient.hpp CLASS NRTThreadedSKMeansClient GROUP MANIPULATION)
+# add_client(KNNClassifier clients/nrt/KNNClassifierClient.hpp CLASS NRTThreadedKNNClassifierClient GROUP MANIPULATION)
+# add_client(KNNRegressor clients/nrt/KNNRegressorClient.hpp CLASS NRTThreadedKNNRegressorClient GROUP MANIPULATION)
+# add_client(Normalize clients/nrt/NormalizeClient.hpp CLASS NRTThreadedNormalizeClient GROUP MANIPULATION)
+# add_client(RobustScale clients/nrt/RobustScaleClient.hpp CLASS NRTThreadedRobustScaleClient GROUP MANIPULATION)
+# add_client(Standardize clients/nrt/StandardizeClient.hpp CLASS NRTThreadedStandardizeClient GROUP MANIPULATION)
+# add_client(PCA clients/nrt/PCAClient.hpp CLASS NRTThreadedPCAClient GROUP MANIPULATION)
+# add_client(MDS clients/nrt/MDSClient.hpp CLASS NRTThreadedMDSClient GROUP MANIPULATION)
+# add_client(UMAP clients/nrt/UMAPClient.hpp CLASS NRTThreadedUMAPClient GROUP MANIPULATION)
+# add_client(MLPRegressor clients/nrt/MLPRegressorClient.hpp CLASS NRTThreadedMLPRegressorClient GROUP MANIPULATION)
+# add_client(MLPClassifier clients/nrt/MLPClassifierClient.hpp CLASS NRTThreadedMLPClassifierClient GROUP MANIPULATION)
+# add_client(Grid clients/nrt/GridClient.hpp CLASS NRTThreadedGridClient GROUP MANIPULATION)
+add_client(LSTMClassifier clients/nrt/LSTMClassifierClient.hpp CLASS NRTThreadedLSTMClassifierClient GROUP MANIPULATION)
+add_client(LSTMRegressor clients/nrt/LSTMRegressorClient.hpp CLASS NRTThreadedLSTMRegressorClient GROUP MANIPULATION)
+add_client(LSTMForecaster clients/nrt/LSTMForecasterClient.hpp CLASS NRTThreadedLSTMForecasterClient GROUP MANIPULATION)

include/algorithms/public/DTW.hpp

@@ -0,0 +1,238 @@
+/*
+Part of the Fluid Corpus Manipulation Project (http://www.flucoma.org/)
+Copyright University of Huddersfield.
+Licensed under the BSD-3 License.
+See license.md file in the project root for full license information.
+This project has received funding from the European Research Council (ERC)
+under the European Union’s Horizon 2020 research and innovation programme
+(grant agreement No 725899).
+*/
+
+#pragma once
+
+#include "../util/FluidEigenMappings.hpp"
+#include "../../data/FluidDataSet.hpp"
+#include "../../data/FluidIndex.hpp"
+#include "../../data/FluidMemory.hpp"
+#include "../../data/FluidTensor.hpp"
+#include "../../data/TensorTypes.hpp"
+#include <Eigen/Core>
+#include <cstddef>
+#include <iterator>
+#include <random>
+
+namespace fluid {
+namespace algorithm {
+
+
+enum class DTWConstraint { kUnconstrained, kIkatura, kSakoeChiba };
+
+// debt of gratitude to the wonderful article on
+// https://rtavenar.github.io/blog/dtw.html a better explanation of DTW than any
+// other algorithm explanation I've seen
+
+class DTW
+{
+  struct Constraint;
+
+public:
+  explicit DTW() = default;
+  ~DTW() = default;
+
+  void init() {}
+  void clear() {}
+
+  index           size() const { return mPNorm; }
+  constexpr index dims() const { return 0; }
+  constexpr index initialized() const { return true; }
+
+  double process(InputRealMatrixView x1, InputRealMatrixView x2,
+                 DTWConstraint constr = DTWConstraint::kUnconstrained,
+                 index         param = 2,
+                 Allocator&    alloc = FluidDefaultAllocator()) const
+  {
+    ScopedEigenMap<Eigen::VectorXd> x1r(x1.cols(), alloc),
+        x2r(x2.cols(), alloc);
+    Constraint constraint(constr, x1.rows(), x2.rows(), param);
+
+    mDistanceMetrics.resize(x1.rows(), x2.rows());
+    mDistanceMetrics.fill(std::numeric_limits<double>::max());
+
+    constraint.iterate([&, this](index r, index c) {
+      x1r = _impl::asEigen<Eigen::Matrix>(x1.row(r));
+      x2r = _impl::asEigen<Eigen::Matrix>(x2.row(c));
+
+      mDistanceMetrics(r, c) = differencePNormToTheP(x1r, x2r);
+
+      if (r > 0 || c > 0)
+      {
+        double minimum = std::numeric_limits<double>::max();
+
+        if (r > 0) minimum = std::min(minimum, mDistanceMetrics(r - 1, c));
+        if (c > 0) minimum = std::min(minimum, mDistanceMetrics(r, c - 1));
+        if (r > 0 && c > 0)
+          minimum = std::min(minimum, mDistanceMetrics(r - 1, c - 1));
+
+        mDistanceMetrics(r, c) += minimum;
+      }
+    });
+
+    return std::pow(mDistanceMetrics(x1.rows() - 1, x2.rows() - 1),
+                    1.0 / mPNorm);
+  }
+
+private:
+  mutable RealMatrix mDistanceMetrics;
+  index              mPNorm{2};
+
+  // P-Norm of the difference vector
+  // Lp{vec} = (|vec[0]|^p + |vec[1]|^p + ... + |vec[n-1]|^p + |vec[n]|^p)^(1/p)
+  // i.e., the 2-norm of a vector is the euclidian distance from the origin
+  //       the 1-norm is the sum of the absolute value of the elements
+  // To the power P since we'll be summing multiple Norms together and they
+  // can combine into a single norm if you calculate the norm of multiple norms
+  // (normception)
+  inline double
+  differencePNormToTheP(const Eigen::Ref<const Eigen::VectorXd>& v1,
+                        const Eigen::Ref<const Eigen::VectorXd>& v2) const
+  {
+    // assert(v1.size() == v2.size());
+    return (v1.array() - v2.array()).abs().pow(mPNorm).sum();
+  }
+
+  // fun little fold operation to do a variadic minimum
+  template <typename... Args>
+  inline static auto min(Args&&... args)
+  {
+    auto m = (args, ...);
+    return ((m = std::min(m, args)), ...);
+  }
+
+  // filter for minimum chaining, if cond evaluates to false then the value
+  // isn't used (never will be the minimum if its the numeric maximum)
+  template <typename T>
+  inline static T useIf(bool cond, T val)
+  {
+    return cond ? val : std::numeric_limits<T>::max();
+  }
+
+  struct Constraint
+  {
+    Constraint(DTWConstraint c, index rows, index cols, float param)
+        : mType{c}, mRows{rows}, mCols{cols}, mParam{param}
+    {
+      // ifn't gradient more than digonal set it to be the diagonal
+      // (sakoe-chiba with radius 0)
+      if (c == DTWConstraint::kIkatura)
+      {
+        float big = std::max(mRows, mCols), smol = std::min(mRows, mCols);
+
+        if (mParam <= big / smol)
+        {
+          mType = DTWConstraint::kSakoeChiba;
+          mParam = 0;
+        }
+      }
+    };
+
+    void iterate(std::function<void(index, index)> f)
+    {
+      index first, last;
+
+      for (index r = 0; r < mRows; ++r)
+      {
+        first = firstCol(r);
+        last = lastCol(r);
+
+        for (index c = first; c <= last; ++c) f(r, c);
+      }
+    };
+
+  private:
+    DTWConstraint mType;
+    index         mRows, mCols;
+    float         mParam; // mParam is either radius (SC) or gradient (Ik)
+
+    inline static index rasterLineMinY(index x1, index y1, float dydx, index x)
+    {
+      return std::round(y1 + (x - x1) * dydx);
+    }
+
+    inline static index rasterLineMinY(index x1, index y1, index x2, index y2,
+                                       index x)
+    {
+      float dy = y2 - y1, dx = x2 - x1;
+      return rasterLineMinY(x1, y1, dy / dx, x);
+    }
+
+    inline static index rasterLineMaxY(index x1, index y1, float dydx, index x)
+    {
+      if (dydx > 1)
+        return rasterLineMinY(x1, y1, dydx, x + 1) - 1;
+      else
+        return rasterLineMinY(x1, y1, dydx, x);
+    }
+
+    inline static index rasterLineMaxY(index x1, index y1, index x2, index y2,
+                                       index x)
+    {
+      float dy = y2 - y1, dx = x2 - x1;
+      return rasterLineMaxY(x1, y1, dy / dx, x);
+    }
+
+    index firstCol(index row)
+    {
+      switch (mType)
+      {
+      case DTWConstraint::kUnconstrained: return 0;
+
+      case DTWConstraint::kIkatura: {
+        index colNorm = rasterLineMinY(mRows - 1, mCols - 1, mParam, row);
+        index colInv = rasterLineMinY(0, 0, 1 / mParam, row);
+
+        index col = std::max(colNorm, colInv);
+
+        return col < 0 ? 0 : col > mCols - 1 ? mCols - 1 : col;
+      }
+
+      case DTWConstraint::kSakoeChiba: {
+        index col = rasterLineMinY(mParam, -mParam, mRows - 1 + mParam,
+                                   mCols - 1 - mParam, row);
+
+        return col < 0 ? 0 : col > mCols - 1 ? mCols - 1 : col;
+      }
+      }
+
+      return 0;
+    };
+
+    index lastCol(index row)
+    {
+      switch (mType)
+      {
+      case DTWConstraint::kUnconstrained: return mCols - 1;
+
+      case DTWConstraint::kIkatura: {
+        index colNorm = rasterLineMaxY(0, 0, mParam, row);
+        index colInv = rasterLineMaxY(mRows - 1, mCols - 1, 1 / mParam, row);
+
+        index col = std::min(colNorm, colInv);
+
+        return col < 0 ? 0 : col > mCols - 1 ? mCols - 1 : col;
+      }
+
+      case DTWConstraint::kSakoeChiba: {
+        index col = rasterLineMaxY(-mParam, mParam, mRows - 1 - mParam,
+                                   mCols - 1 + mParam, row);
+
+        return col < 0 ? 0 : col > mCols - 1 ? mCols - 1 : col;
+      }
+      }
+
+      return mCols - 1;
+    };
+  }; // struct Constraint
+};
+
+} // namespace algorithm
+} // namespace fluid