# Copyright (c) Recommenders contributors.
# Licensed under the MIT License.
import numpy as np
from sklearn.decomposition import PCA
[docs]def length_normalize(matrix):
"""Length normalize the matrix
Args:
matrix (np.ndarray): Input matrix that needs to be normalized
Returns:
Normalized matrix
"""
norms = np.sqrt(np.sum(matrix**2, axis=1))
norms[norms == 0] = 1
return matrix / norms[:, np.newaxis]
[docs]def mean_center(matrix):
"""Performs mean centering across axis 0
Args:
matrix (np.ndarray): Input matrix that needs to be mean centered
"""
avg = np.mean(matrix, axis=0)
matrix -= avg
[docs]def reduce_dims(matrix, target_dim):
"""Reduce dimensionality of the data using PCA.
Args:
matrix (np.ndarray): Matrix of the form (n_sampes, n_features)
target_dim (uint): Dimension to which n_features should be reduced to.
"""
model = PCA(n_components=target_dim)
model.fit(matrix)
return model.transform(matrix)