wehub-resource-sync
64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
# coding:utf-8
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import logging
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import numpy as np
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from scipy.linalg import svd
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from mla.base import BaseEstimator
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np.random.seed(1000)
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class PCA(BaseEstimator):
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y_required = False
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def __init__(self, n_components, solver="svd"):
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"""Principal component analysis (PCA) implementation.
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Transforms a dataset of possibly correlated values into n linearly
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uncorrelated components. The components are ordered such that the first
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has the largest possible variance and each following component as the
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largest possible variance given the previous components. This causes
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the early components to contain most of the variability in the dataset.
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Parameters
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----------
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n_components : int
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solver : str, default 'svd'
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{'svd', 'eigen'}
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"""
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self.solver = solver
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self.n_components = n_components
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self.components = None
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self.mean = None
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def fit(self, X, y=None):
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self.mean = np.mean(X, axis=0)
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self._decompose(X)
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def _decompose(self, X):
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# Mean centering
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X = X.copy()
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X -= self.mean
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if self.solver == "svd":
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_, s, Vh = svd(X, full_matrices=True)
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elif self.solver == "eigen":
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s, Vh = np.linalg.eig(np.cov(X.T))
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Vh = Vh.T
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s_squared = s**2
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variance_ratio = s_squared / s_squared.sum()
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logging.info(
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"Explained variance ratio: %s" % (variance_ratio[0 : self.n_components])
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)
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self.components = Vh[0 : self.n_components]
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def transform(self, X):
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X = X.copy()
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X -= self.mean
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return np.dot(X, self.components.T)
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def _predict(self, X=None):
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return self.transform(X)
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