chore: import upstream snapshot with attribution

mla/pca.py

@@ -0,0 +1,63 @@
+# coding:utf-8
+import logging
+
+import numpy as np
+from scipy.linalg import svd
+
+from mla.base import BaseEstimator
+
+np.random.seed(1000)
+
+
+class PCA(BaseEstimator):
+    y_required = False
+
+    def __init__(self, n_components, solver="svd"):
+        """Principal component analysis (PCA) implementation.
+
+        Transforms a dataset of possibly correlated values into n linearly
+        uncorrelated components. The components are ordered such that the first
+        has the largest possible variance and each following component as the
+        largest possible variance given the previous components. This causes
+        the early components to contain most of the variability in the dataset.
+
+        Parameters
+        ----------
+        n_components : int
+        solver : str, default 'svd'
+            {'svd', 'eigen'}
+        """
+        self.solver = solver
+        self.n_components = n_components
+        self.components = None
+        self.mean = None
+
+    def fit(self, X, y=None):
+        self.mean = np.mean(X, axis=0)
+        self._decompose(X)
+
+    def _decompose(self, X):
+        # Mean centering
+        X = X.copy()
+        X -= self.mean
+
+        if self.solver == "svd":
+            _, s, Vh = svd(X, full_matrices=True)
+        elif self.solver == "eigen":
+            s, Vh = np.linalg.eig(np.cov(X.T))
+            Vh = Vh.T
+
+        s_squared = s**2
+        variance_ratio = s_squared / s_squared.sum()
+        logging.info(
+            "Explained variance ratio: %s" % (variance_ratio[0 : self.n_components])
+        )
+        self.components = Vh[0 : self.n_components]
+
+    def transform(self, X):
+        X = X.copy()
+        X -= self.mean
+        return np.dot(X, self.components.T)
+
+    def _predict(self, X=None):
+        return self.transform(X)