minMean = mean
if mean > maxMean:
maxMean = mean
if std > maxStd:
maxStd = std
l = 2*(minMean - maxStd)
r = 2*(maxMean + maxStd)
for m, s in toPlot:
normDist(m,s,l,r)
formt = FormatJson(sys.argv[1])
naive = NaiveBayes(formt.data())
print "Total Accuracy (cross validate) = " + str(naive.crossValidate(10))
labels = formt.channels()
counts = [[0]*len(labels) for i in range(len(labels))]
for label, data in formt.data():
expectedChn, _ = naive.predict(data)
# Confusion matrix
correct = labels.index(label)
actual = labels.index(expectedChn)
counts[correct][actual] += 1
print counts
labels = [''] + labels
from digit import Digit
from data_processer import DatasetParser
from naive import NaiveBayes
import time
beg = time.time()
parser_train = DatasetParser("trainingimages.txt","traininglabels.txt")
parser_test = DatasetParser("testimages.txt","testlabels.txt")
train_samples = parser_train.read()
test_samples = parser_test.read()
classifier = NaiveBayes(train_samples,test_samples)
print "Training starts..."
classifier.train()
print "Testing starts..."
predictions = classifier.test()
digit_counts = 10*[0]
digit_predicted_counts = 10*[0]
true_positives = 10*[0]
confusion_matrix = [[0 for x in range(10)] for x in range(10)] ;
