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fix: use var_ instead of deprecated sigma_ in GaussianNB test #101

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fix: use var_ instead of deprecated sigma_ in GaussianNB test #101

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6 changes: 3 additions & 3 deletions numpy_ml/tests/test_naive_bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,13 +42,13 @@ def test_GaussianNB(N=10):

np.testing.assert_almost_equal(jointi, jointi_mine)

n_jk = -0.5 * np.sum(np.log(2.0 * np.pi * sklearn_NB.sigma_[j, :] + eps))
n_jk = -0.5 * np.sum(np.log(2.0 * np.pi * sklearn_NB.var_[j, :] + eps))
n_jk_mine = -0.5 * np.sum(np.log(2.0 * np.pi * P["sigma"][j] + eps))

np.testing.assert_almost_equal(n_jk_mine, n_jk)

n_jk2 = n_jk - 0.5 * np.sum(
((X_test - sklearn_NB.theta_[j, :]) ** 2) / (sklearn_NB.sigma_[j, :]), 1
((X_test - sklearn_NB.theta_[j, :]) ** 2) / (sklearn_NB.var_[j, :]), 1
)

n_jk2_mine = n_jk_mine - 0.5 * np.sum(
Expand All @@ -63,7 +63,7 @@ def test_GaussianNB(N=10):

np.testing.assert_almost_equal(P["prior"], sklearn_NB.class_prior_)
np.testing.assert_almost_equal(P["mean"], sklearn_NB.theta_)
np.testing.assert_almost_equal(P["sigma"], sklearn_NB.sigma_)
np.testing.assert_almost_equal(P["sigma"], sklearn_NB.var_)
np.testing.assert_almost_equal(
sklearn_NB._joint_log_likelihood(X_test),
NB._log_posterior(X_test),
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2 changes: 1 addition & 1 deletion numpy_ml/utils/data_structures.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import heapq
from copy import copy
from collections import Hashable
from collections.abc import Hashable

import numpy as np

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