https://github.com/tensorly/tensorly
Revision 1bb217a077d6fa1d507f963a60da81adfd099d79 authored by Jean Kossaifi on 14 July 2017, 03:03:33 UTC, committed by GitHub on 14 July 2017, 03:03:33 UTC
Improving partial_svd by omitting full svd matrices when possible
Tip revision: 1bb217a077d6fa1d507f963a60da81adfd099d79 authored by Jean Kossaifi on 14 July 2017, 03:03:33 UTC
Merge pull request #7 from chubei/master
Merge pull request #7 from chubei/master
Tip revision: 1bb217a
regression.py
import numpy as np
# Author: Jean Kossaifi <jean.kossaifi+tensors@gmail.com>
def MSE(y_true, y_pred):
"""Returns the mean squared error between the two predictions
Parameters
----------
y_true : array of shape (n_samples, )
Ground truth (correct) target values.
y_pred : array of shape (n_samples, )
Estimated target values.
Returns
-------
float
"""
return np.mean((y_true - y_pred) ** 2)
def RMSE(y_true, y_pred):
"""Returns the regularised mean squared error between the two predictions
(the square-root is applied to the mean_squared_error)
Parameters
----------
y_true : array of shape (n_samples, )
Ground truth (correct) target values.
y_pred : array of shape (n_samples, )
Estimated target values.
Returns
-------
float
"""
return np.sqrt(MSE(y_true, y_pred))
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