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