wehub-resource-sync
49 lines
1.2 KiB
Python
Executable File
49 lines
1.2 KiB
Python
Executable File
import numpy as np
|
|
from prml.linear.regression import Regression
|
|
|
|
|
|
class LinearRegression(Regression):
|
|
"""
|
|
Linear regression model
|
|
y = X @ w
|
|
t ~ N(t|X @ w, var)
|
|
"""
|
|
|
|
def fit(self, X:np.ndarray, t:np.ndarray):
|
|
"""
|
|
perform least squares fitting
|
|
|
|
Parameters
|
|
----------
|
|
X : (N, D) np.ndarray
|
|
training independent variable
|
|
t : (N,) np.ndarray
|
|
training dependent variable
|
|
"""
|
|
self.w = np.linalg.pinv(X) @ t
|
|
self.var = np.mean(np.square(X @ self.w - t))
|
|
|
|
def predict(self, X:np.ndarray, return_std:bool=False):
|
|
"""
|
|
make prediction given input
|
|
|
|
Parameters
|
|
----------
|
|
X : (N, D) np.ndarray
|
|
samples to predict their output
|
|
return_std : bool, optional
|
|
returns standard deviation of each predition if True
|
|
|
|
Returns
|
|
-------
|
|
y : (N,) np.ndarray
|
|
prediction of each sample
|
|
y_std : (N,) np.ndarray
|
|
standard deviation of each predition
|
|
"""
|
|
y = X @ self.w
|
|
if return_std:
|
|
y_std = np.sqrt(self.var) + np.zeros_like(y)
|
|
return y, y_std
|
|
return y
|