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.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "tests"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

README.md

@@ -1,3 +1,4 @@
+[![Open in Visual Studio Code](https://classroom.github.com/assets/open-in-vscode-2e0aaae1b6195c2367325f4f02e2d04e9abb55f0b24a779b69b11b9e10269abc.svg)](https://classroom.github.com/online_ide?assignment_repo_id=17425364&assignment_repo_type=AssignmentRepo)
 # MiniTorch Module 0
 
 <img src="https://minitorch.github.io/minitorch.svg" width="50%">

minitorch/datasets.py

@@ -67,19 +67,29 @@ def circle(N):
 
 
 def spiral(N):
-
     def x(t):
         return t * math.cos(t) / 20.0
 
     def y(t):
         return t * math.sin(t) / 20.0
-    X = [(x(10.0 * (float(i) / (N // 2))) + 0.5, y(10.0 * (float(i) / (N //
-        2))) + 0.5) for i in range(5 + 0, 5 + N // 2)]
-    X = X + [(y(-10.0 * (float(i) / (N // 2))) + 0.5, x(-10.0 * (float(i) /
-        (N // 2))) + 0.5) for i in range(5 + 0, 5 + N // 2)]
+
+    X = [
+        (x(10.0 * (float(i) / (N // 2))) + 0.5, y(10.0 * (float(i) / (N // 2))) + 0.5)
+        for i in range(5 + 0, 5 + N // 2)
+    ]
+    X = X + [
+        (y(-10.0 * (float(i) / (N // 2))) + 0.5, x(-10.0 * (float(i) / (N // 2))) + 0.5)
+        for i in range(5 + 0, 5 + N // 2)
+    ]
     y2 = [0] * (N // 2) + [1] * (N // 2)
     return Graph(N, X, y2)
 
 
-datasets = {'Simple': simple, 'Diag': diag, 'Split': split, 'Xor': xor,
-    'Circle': circle, 'Spiral': spiral}
+datasets = {
+    "Simple": simple,
+    "Diag": diag,
+    "Split": split,
+    "Xor": xor,
+    "Circle": circle,
+    "Spiral": spiral,
+}

minitorch/operators.py

@@ -3,28 +3,98 @@
 import math
 
 # ## Task 0.1
-from typing import Callable, Iterable
 
 #
 # Implementation of a prelude of elementary functions.
 
+
 # Mathematical functions:
 # - mul
+def mul(x: float, y: float) -> float:
+    return x * y
+
+
 # - id
+def id(x: float) -> float:
+    return x
+
+
 # - add
+def add(x: float, y: float) -> float:
+    return x + y
+
+
 # - neg
+def neg(x: float) -> float:
+    return -x
+
+
 # - lt
+def lt(x: float, y: float) -> bool:
+    return x < y
+
+
 # - eq
+def eq(x: float, y: float) -> bool:
+    return x == y
+
+
 # - max
+def max(x: float, y: float) -> float:
+    return y if lt(x, y) else x
+
+
 # - is_close
+def is_close(x: float, y: float) -> bool:
+    return abs(x - y) <= 0.01
+
+
 # - sigmoid
+def sigmoid(x: float) -> float:
+    if x >= 0:
+        return 1.0 / (1.0 + math.e ** (-x))
+    else:
+        return math.e**x / (1.0 + math.e**x)
+
+
 # - relu
+def relu(x: float) -> float:
+    if x >= 0:
+        return x
+    else:
+        return 0
+
+
 # - log
+def log(x: float) -> float:
+    return math.log(x)
+
+
 # - exp
+def exp(x: float) -> float:
+    return math.e**x
+
+
 # - log_back
+def log_back(x: float, d: float) -> float:
+    return d * (1 / x)
+
+
 # - inv
+def inv(x: float) -> float:
+    return 1 / x
+
+
 # - inv_back
+def inv_back(x: float, d: float) -> float:
+    return -d / (x**2)
+
+
 # - relu_back
+def relu_back(x: float, d: float) -> float:
+    return d if x > 0 else 0
+
+
 #
 # For sigmoid calculate as:
 # $f(x) =  \frac{1.0}{(1.0 + e^{-x})}$ if x >=0 else $\frac{e^x}{(1.0 + e^{x})}$
@@ -44,11 +114,27 @@
 # - zipWith
 # - reduce
 #
+
+
 # Use these to implement
 # - negList : negate a list
+def negList(a: list) -> list:
+    return a
+
+
 # - addLists : add two lists together
+def addLists(a: list, b: list) -> list:
+    return a + b
+
+
 # - sum: sum lists
+def sum(a: list) -> float:
+    return a[0]
+
+
 # - prod: take the product of lists
+def prod(a: list) -> float:
+    return a[0]
 
 
 # TODO: Implement for Task 0.3.

requirements.extra.txt

@@ -1,11 +1,11 @@
+altair==4.2.2
 datasets==2.4.0
 embeddings==0.0.8
+networkx==3.3
 plotly==4.14.3
 pydot==1.4.1
 python-mnist
 streamlit==1.12.0
 streamlit-ace
 torch
 watchdog==1.0.2
-altair==4.2.2
-networkx==3.3

tests/test_operators.py

@@ -22,7 +22,6 @@
     prod,
     relu,
     relu_back,
-    sigmoid,
 )
 
 from .strategies import assert_close, small_floats