Python GaussianNaiveBayes Examples

Example #1

File: nbc.py Project: rancho93/benchmarks

    def RunNBCShogun(q):
      totalTimer = Timer()
      
      Log.Info("Loading dataset", self.verbose)
      try:
        # Load train and test dataset.
        trainData = np.genfromtxt(self.dataset[0], delimiter=',')
        testData = np.genfromtxt(self.dataset[1], delimiter=',')

        # Labels are the last row of the training set.
        labels = MulticlassLabels(trainData[:, (trainData.shape[1] - 1)])

        with totalTimer:
          # Transform into features.
          trainFeat = RealFeatures(trainData[:,:-1].T)
          testFeat = RealFeatures(testData.T)

          # Create and train the classifier.
          nbc = GaussianNaiveBayes(trainFeat, labels)
          nbc.train()
          # Run Naive Bayes Classifier on the test dataset.
          nbc.apply(testFeat).get_labels()
      except Exception as e:
        q.put(-1)
        return -1

      time = totalTimer.ElapsedTime()
      q.put(time)
      return time

Example #2

File: classifier_gaussiannaivebayes_modular.py Project: Siddharthk/shogun

def classifier_gnb_modular(fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat):
	from shogun.Features import RealFeatures, Labels
	from shogun.Classifier import GaussianNaiveBayes

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)
	labels=Labels(label_train_multiclass)

	gnb=GaussianNaiveBayes(feats_train, labels)
	gnb_train = gnb.train()
	output=gnb.classify(feats_test).get_labels()
	return gnb,gnb_train,output

Example #3

def classifier_gnb_modular(fm_train_real=traindat,
                           fm_test_real=testdat,
                           label_train_multiclass=label_traindat):
    from shogun.Features import RealFeatures, Labels
    from shogun.Classifier import GaussianNaiveBayes

    feats_train = RealFeatures(fm_train_real)
    feats_test = RealFeatures(fm_test_real)
    labels = Labels(label_train_multiclass)

    gnb = GaussianNaiveBayes(feats_train, labels)
    gnb_train = gnb.train()
    output = gnb.classify(feats_test).get_labels()
    return gnb, gnb_train, output

Example #4

        def RunNBCShogun(q):
            totalTimer = Timer()

            Log.Info("Loading dataset", self.verbose)
            try:
                # Load train and test dataset.
                trainData = np.genfromtxt(self.dataset[0], delimiter=',')
                testData = np.genfromtxt(self.dataset[1], delimiter=',')

                # Labels are the last row of the training set.
                labels = MulticlassLabels(trainData[:,
                                                    (trainData.shape[1] - 1)])

                with totalTimer:
                    # Transform into features.
                    trainFeat = RealFeatures(trainData[:, :-1].T)
                    testFeat = RealFeatures(testData.T)

                    # Create and train the classifier.
                    nbc = GaussianNaiveBayes(trainFeat, labels)
                    nbc.train()
                    # Run Naive Bayes Classifier on the test dataset.
                    nbc.apply(testFeat).get_labels()
            except Exception as e:
                q.put(-1)
                return -1

            time = totalTimer.ElapsedTime()
            q.put(time)
            return time

Example #5

import numpy as np
import os
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import GaussianNaiveBayes

# Load the data.
f = open(os.path.dirname(__file__) + '../data/fisheriris.data')
data = np.fromfile(f, dtype=np.float64, sep=' ')
data = data.reshape(-1, 4)
f.close()

f = open(os.path.dirname(__file__) + '../data/fisheriris_label.csv')
label = np.fromfile(f, dtype=np.float64, sep=' ')
f.close()

# Naive Bayes classifier.
feat = RealFeatures(data.T)
test = RealFeatures(data.T)
labels = MulticlassLabels(label)
nbc = GaussianNaiveBayes(feat, labels)
nbc.train()

# Predict labels.
results = nbc.apply(test).get_labels()

print results