adding all work done so far (lessons 1 - 5)

python/Supervised Learning/Perceptron Algoritm/9. Perceptron Algorithm/perceptron.py

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+import numpy as np
+# Setting the random seed, feel free to change it and see different solutions.
+np.random.seed(42)
+import pandas as pd
+
+
+def stepFunction(t):
+    if t >= 0:
+        return 1
+    return 0
+
+
+def prediction(X, W, b):
+    return stepFunction((np.matmul(X, W) + b)[0])
+
+# TODO: Fill in the code below to implement the perceptron trick.
+# The function should receive as inputs the data X, the labels y,
+# the weights W (as an array), and the bias b,
+# update the weights and bias W, b, according to the perceptron algorithm,
+# and return W and b.
+
+
+def perceptronStep(X, y, W, b, learn_rate=0.01):
+    for i in range(len(X)):
+        y_hat = prediction(X[i], W, b)
+        if y[i] - y_hat == 1:
+            W[0] += X[i][0] * learn_rate
+            W[1] += X[i][1] * learn_rate
+            b += learn_rate
+        elif y[i] - y_hat == -1:
+            W[0] -= X[i][0] * learn_rate
+            W[1] -= X[i][1] * learn_rate
+            b -= learn_rate
+    return W, b
+
+
+# This function runs the perceptron algorithm repeatedly on the dataset,
+# and returns a few of the boundary lines obtained in the iterations,
+# for plotting purposes.
+# Feel free to play with the learning rate and the num_epochs,
+# and see your results plotted below.
+
+
+def trainPerceptronAlgorithm(X, y, learn_rate=0.01, num_epochs=25):
+    x_min, x_max = min(X.T[0]), max(X.T[0])
+    y_min, y_max = min(X.T[1]), max(X.T[1])
+    W = np.array(np.random.rand(2, 1))
+    b = np.random.rand(1)[0] + x_max
+    # These are the solution lines that get plotted below.
+    boundary_lines = []
+    for i in range(num_epochs):
+        # In each epoch, we apply the perceptron step.
+        W, b = perceptronStep(X, y, W, b, learn_rate)
+        boundary_lines.append((-W[0] / W[1], -b / W[1]))
+    return boundary_lines
+
+
+df = pd.read_csv('data.csv', header=None)
+
+X = df.iloc[:, :-1]
+X = np.array(X)
+y = df.iloc[:, 1]
+y = np.array(y)
+
+print(trainPerceptronAlgorithm(X, y))
+# print(X)