Uploaded Test files

venv/Lib/site-packages/sklearn/linear_model/tests/test_perceptron.py

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+import numpy as np
+import scipy.sparse as sp
+
+from sklearn.utils._testing import assert_array_almost_equal
+from sklearn.utils._testing import assert_raises
+
+from sklearn.utils import check_random_state
+from sklearn.datasets import load_iris
+from sklearn.linear_model import Perceptron
+
+iris = load_iris()
+random_state = check_random_state(12)
+indices = np.arange(iris.data.shape[0])
+random_state.shuffle(indices)
+X = iris.data[indices]
+y = iris.target[indices]
+X_csr = sp.csr_matrix(X)
+X_csr.sort_indices()
+
+
+class MyPerceptron:
+
+    def __init__(self, n_iter=1):
+        self.n_iter = n_iter
+
+    def fit(self, X, y):
+        n_samples, n_features = X.shape
+        self.w = np.zeros(n_features, dtype=np.float64)
+        self.b = 0.0
+
+        for t in range(self.n_iter):
+            for i in range(n_samples):
+                if self.predict(X[i])[0] != y[i]:
+                    self.w += y[i] * X[i]
+                    self.b += y[i]
+
+    def project(self, X):
+        return np.dot(X, self.w) + self.b
+
+    def predict(self, X):
+        X = np.atleast_2d(X)
+        return np.sign(self.project(X))
+
+
+def test_perceptron_accuracy():
+    for data in (X, X_csr):
+        clf = Perceptron(max_iter=100, tol=None, shuffle=False)
+        clf.fit(data, y)
+        score = clf.score(data, y)
+        assert score > 0.7
+
+
+def test_perceptron_correctness():
+    y_bin = y.copy()
+    y_bin[y != 1] = -1
+
+    clf1 = MyPerceptron(n_iter=2)
+    clf1.fit(X, y_bin)
+
+    clf2 = Perceptron(max_iter=2, shuffle=False, tol=None)
+    clf2.fit(X, y_bin)
+
+    assert_array_almost_equal(clf1.w, clf2.coef_.ravel())
+
+
+def test_undefined_methods():
+    clf = Perceptron(max_iter=100)
+    for meth in ("predict_proba", "predict_log_proba"):
+        assert_raises(AttributeError, lambda x: getattr(clf, x), meth)