chore: import upstream snapshot with attribution

examples/pca.py

@@ -0,0 +1,39 @@
+try:
+    from sklearn.model_selection import train_test_split
+except ImportError:
+    from sklearn.cross_validation import train_test_split
+from sklearn.datasets import make_classification
+
+from mla.linear_models import LogisticRegression
+from mla.metrics import accuracy
+from mla.pca import PCA
+
+# logging.basicConfig(level=logging.DEBUG)
+
+# Generate a random binary classification problem.
+X, y = make_classification(
+    n_samples=1000,
+    n_features=100,
+    n_informative=75,
+    random_state=1111,
+    n_classes=2,
+    class_sep=2.5,
+)
+
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.25, random_state=1111
+)
+
+for s in ["svd", "eigen"]:
+    p = PCA(15, solver=s)
+
+    # fit PCA with training data, not entire dataset
+    p.fit(X_train)
+    X_train_reduced = p.transform(X_train)
+    X_test_reduced = p.transform(X_test)
+
+    model = LogisticRegression(lr=0.001, max_iters=2500)
+    model.fit(X_train_reduced, y_train)
+    predictions = model.predict(X_test_reduced)
+    print("Classification accuracy for %s PCA: %s" % (s, accuracy(y_test, predictions)))