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

2019-07-10 19:58:53 +01:00
parent 8085149a49
commit b982957daf
37 changed files with 19407 additions and 0 deletions
--- a/Operators/perceptron.py
+++ b/Operators/perceptron.py
@@ -0,0 +1,29 @@
+import pandas as pd
+
+# TODO: Set weight1, weight2, and bias
+weight1 = 0.0
+weight2 = 0.0
+bias = 0.0
+
+
+# DON'T CHANGE ANYTHING BELOW
+# Inputs and outputs
+test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
+correct_outputs = [False, False, False, True]
+outputs = []
+
+# Generate and check output
+for test_input, correct_output in zip(test_inputs, correct_outputs):
+    linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
+    output = int(linear_combination >= 0)
+    is_correct_string = 'Yes' if output == correct_output else 'No'
+    outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])
+
+# Print output
+num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
+output_frame = pd.DataFrame(outputs, columns=['Input 1', '  Input 2', '  Linear Combination', '  Activation Output', '  Is Correct'])
+if not num_wrong:
+    print('Nice!  You got it all correct.\n')
+else:
+    print('You got {} wrong.  Keep trying!\n'.format(num_wrong))
+print(output_frame.to_string(index=False))
--- a/Operators/perceptron_and.py
+++ b/Operators/perceptron_and.py
@@ -0,0 +1,29 @@
+import pandas as pd
+
+# TODO: Set weight1, weight2, and bias
+weight1 = 1.0
+weight2 = 1.0
+bias = -1.25
+
+
+# DON'T CHANGE ANYTHING BELOW
+# Inputs and outputs
+test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
+correct_outputs = [False, False, False, True]
+outputs = []
+
+# Generate and check output
+for test_input, correct_output in zip(test_inputs, correct_outputs):
+    linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
+    output = int(linear_combination >= 0)
+    is_correct_string = 'Yes' if output == correct_output else 'No'
+    outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])
+
+# Print output
+num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
+output_frame = pd.DataFrame(outputs, columns=['Input 1', '  Input 2', '  Linear Combination', '  Activation Output', '  Is Correct'])
+if not num_wrong:
+    print('Nice!  You got it all correct.\n')
+else:
+    print('You got {} wrong.  Keep trying!\n'.format(num_wrong))
+print(output_frame.to_string(index=False))
--- a/Operators/perceptron_not.py
+++ b/Operators/perceptron_not.py
@@ -0,0 +1,29 @@
+import pandas as pd
+
+# TODO: Set weight1, weight2, and bias
+weight1 = 0.0
+weight2 = -1.0
+bias = 0.8
+
+
+# DON'T CHANGE ANYTHING BELOW
+# Inputs and outputs
+test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
+correct_outputs = [True, False, True, False]
+outputs = []
+
+# Generate and check output
+for test_input, correct_output in zip(test_inputs, correct_outputs):
+    linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
+    output = int(linear_combination >= 0)
+    is_correct_string = 'Yes' if output == correct_output else 'No'
+    outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])
+
+# Print output
+num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
+output_frame = pd.DataFrame(outputs, columns=['Input 1', '  Input 2', '  Linear Combination', '  Activation Output', '  Is Correct'])
+if not num_wrong:
+    print('Nice!  You got it all correct.\n')
+else:
+    print('You got {} wrong.  Keep trying!\n'.format(num_wrong))
+print(output_frame.to_string(index=False))
--- a/Learning/Perceptron
+++ b/Learning/Perceptron
@@ -0,0 +1,100 @@
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--- a/Algorithm/perceptron.py
+++ b/Algorithm/perceptron.py
@@ -0,0 +1,66 @@
+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)