Created
October 13, 2019 07:58
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linreg_gradient_descent
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b0, b1 = 0.0, 1.0 | |
lr = 0.001 | |
epochs = 10000 | |
error = [] | |
# run 10000 times | |
for epoch in range(epochs): | |
# initialize to 0 -> cost of epoch, Jb_0, Jb_1 | |
epoch_cost, cost_b0, cost_b1 = 0, 0, 0 | |
for i in range(len(x)): | |
# make prediction | |
y_pred = (b0 + b1*x[i]) | |
# append squared error | |
epoch_cost += (y[i] - y_pred)**2 | |
for j in range(len(x)): | |
# partial derivative of b0 and b1 for current row | |
partial_wrt_b0 = -2 * (y[j] - (b0 + b1*x[j])) | |
partial_wrt_b1 = (-2 * x[j]) * (y[j] - (b0 + b1*x[j])) | |
# increase cost of coeffs | |
cost_b0 += partial_wrt_b0 | |
cost_b1 += partial_wrt_b1 | |
# calculate new coeffs | |
b0 = b0 - lr * cost_b0 | |
b1 = b1 - lr * cost_b1 | |
# keep track of errors - for visualization purposes | |
error.append(epoch_cost) |
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