wehub-resource-sync
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
# coding:utf-8
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import numpy as np
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from mla.base import BaseEstimator
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from mla.neuralnet.activations import softmax
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class NaiveBayesClassifier(BaseEstimator):
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"""Gaussian Naive Bayes."""
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# Binary problem.
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n_classes = 2
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def fit(self, X, y=None):
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self._setup_input(X, y)
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# Check target labels
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assert list(np.unique(y)) == [0, 1]
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# Mean and variance for each class and feature combination
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self._mean = np.zeros((self.n_classes, self.n_features), dtype=np.float64)
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self._var = np.zeros((self.n_classes, self.n_features), dtype=np.float64)
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self._priors = np.zeros(self.n_classes, dtype=np.float64)
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for c in range(self.n_classes):
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# Filter features by class
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X_c = X[y == c]
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# Calculate mean, variance, prior for each class
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self._mean[c, :] = X_c.mean(axis=0)
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self._var[c, :] = X_c.var(axis=0)
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self._priors[c] = X_c.shape[0] / float(X.shape[0])
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def _predict(self, X=None):
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# Apply _predict_proba for each row
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predictions = np.apply_along_axis(self._predict_row, 1, X)
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# Normalize probabilities so that each row will sum up to 1.0
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return softmax(predictions)
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def _predict_row(self, x):
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"""Predict log likelihood for given row."""
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output = []
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for y in range(self.n_classes):
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prior = np.log(self._priors[y])
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posterior = np.log(self._pdf(y, x)).sum()
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prediction = prior + posterior
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output.append(prediction)
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return output
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def _pdf(self, n_class, x):
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"""Calculate Gaussian PDF for each feature."""
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mean = self._mean[n_class]
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var = self._var[n_class]
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numerator = np.exp(-((x - mean) ** 2) / (2 * var))
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denominator = np.sqrt(2 * np.pi * var)
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return numerator / denominator
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