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Fix gradient descent for LinearLearning #414

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Mar 25, 2017
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Fix gradient descent for LinearLearning #414

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4 changes: 3 additions & 1 deletion learning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -635,6 +635,7 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100):
idx_i = dataset.inputs
idx_t = dataset.target # As of now, dataset.target gives only one index.
examples = dataset.examples
num_examples = len(examples)

# X transpose
X_col = [dataset.values[i] for i in idx_i] # vertical columns of X
Expand All @@ -657,7 +658,8 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100):

# update weights
for i in range(len(w)):
w[i] = w[i] - learning_rate * dotproduct(err, X_col[i])
w[i] = w[i] + learning_rate * (dotproduct(err, X_col[i]) / num_examples)


def predict(example):
x = [1] + example
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