def _calculate_variance_reduction(self, y, y1, y2):
var_tot = calculate_variance(y)
var_1 = calculate_variance(y1)
var_2 = calculate_variance(y2)
frac_1 = len(y1) / len(y)
frac_2 = len(y2) / len(y)
variance_reduction = var_tot - (frac_1 * var_1 + frac_2 * var_2)
return sum(variance_reduction)
def _calculate_variance_reduction(self, y, y1, y2):
var_tot = calculate_variance(y)
var_1 = calculate_variance(y1)
var_2 = calculate_variance(y2)
frac_1 = len(y1) / len(y)
frac_2 = len(y2) / len(y)
# Calculate the variance reduction
variance_reduction = var_tot - (frac_1 * var_1 + frac_2 * var_2)
#print("variance_reduction:",variance_reduction)
return sum(variance_reduction)
def _calculate_variance_reduction(self, y, y1, y2):
var_tot = calculate_variance(y)
var_1 = calculate_variance(y1)
var_2 = calculate_variance(y2)
frac_1 = len(y1) / len(y)
frac_2 = len(y2) / len(y)
# Calculate the variance reduction
variance_reduction = var_tot - (frac_1 * var_1 + frac_2 * var_2)
return sum(variance_reduction)
# Initialize array
x3 = np.empty((0, n))
# Append x3
for x1, x2 in x:
temp = sin(x1) * x2
x3 = np.append(x3, temp)
# Build the X matrix
X = np.insert(x, 2, x3, axis=1)
# Make the predictions of the model
y1_pred = linear_regressor.predict(X)
MSE = evaluate_predictions(y1_pred, y)
e_lr = MSE * n
# calculate variance squared
s_lr = calculate_variance(y, y1_pred, e_lr)
# Model from Task 2
# Make the prediction
y2_pred = regressor.predict(x_test)
y2_pred = y2_pred.reshape(y_test.shape)
MSE = evaluate_predictions(y2_pred, y_test)
e_nn = MSE * len(x_test)
# calculate variance squared
s_nn = calculate_variance(y_test, y2_pred, e_nn)
# Print statistics and finish
print("\nTask 1 model: Mean: {} -- Variance Squared: {}".format(
e_lr, s_lr))
