add gaussian nb unsupervised deep learning

Author: bob7783

unsupervised_class2/gaussian_nb.py

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+# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
+# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
+from __future__ import print_function, division
+from future.utils import iteritems
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
+import numpy as np
+from sklearn.decomposition import PCA
+# from sklearn.naive_bayes import GaussianNB # doesn't have smoothing
+from scipy.stats import norm
+from scipy.stats import multivariate_normal as mvn
+from util import getKaggleMNIST
+
+
+class GaussianNB(object):
+  def fit(self, X, Y, smoothing=1e-2):
+    self.gaussians = dict()
+    self.priors = dict()
+    labels = set(Y)
+    for c in labels:
+      current_x = X[Y == c]
+      self.gaussians[c] = {
+        'mean': current_x.mean(axis=0),
+        'var': current_x.var(axis=0) + smoothing,
+      }
+      self.priors[c] = float(len(Y[Y == c])) / len(Y)
+
+  def score(self, X, Y):
+    P = self.predict(X)
+    return np.mean(P == Y)
+
+  def predict(self, X):
+    N, D = X.shape
+    K = len(self.gaussians)
+    P = np.zeros((N, K))
+    for c, g in iteritems(self.gaussians):
+      mean, var = g['mean'], g['var']
+      P[:,c] = mvn.logpdf(X, mean=mean, cov=var) + np.log(self.priors[c])
+    return np.argmax(P, axis=1)
+
+
+# get data
+Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
+
+# try NB by itself
+model1 = GaussianNB()
+model1.fit(Xtrain, Ytrain)
+print("NB train score:", model1.score(Xtrain, Ytrain))
+print("NB test score:", model1.score(Xtest, Ytest))
+
+# try NB with PCA first
+pca = PCA(n_components=50)
+Ztrain = pca.fit_transform(Xtrain)
+Ztest = pca.transform(Xtest)
+
+model2 = GaussianNB()
+model2.fit(Ztrain, Ytrain)
+print("NB+PCA train score:", model2.score(Ztrain, Ytrain))
+print("NB+PCA test score:", model2.score(Ztest, Ytest))