if labels_train[ii] == 0
]
grade_slow = [
features_train[ii][0] for ii in range(0, len(features_train))
if labels_train[ii] == 1
]
bumpy_slow = [
features_train[ii][1] for ii in range(0, len(features_train))
if labels_train[ii] == 1
]
clf = classify(features_train, labels_train)
# draw the decision boundary with the text points overlaid
prettyPicture(clf, features_test, labels_test)
output_image("test.png", "png", open("test.png", "rb").read())
# compare tha accuracy by hand
count = 0.0 # have to be in floating point
predict = clf.predict(features_test)
for i in range(0, len(predict)):
if predict[i] == labels_test[i]:
count += 1
accuracy = count / (len(predict))
print 'self-calculate: ', accuracy
# using built-in function of sklearn
acc = NBAccuracy(features_train, labels_train, features_test, labels_test)
print 'using clf.score: ', acc
# or
print 'accuracy_score function', accuracy_score(predict, labels_test)
def submitAccuracy():
accuracy = NBAccuracy(features_train, labels_train, features_test,
labels_test)
return accuracy
grade_fast = [
features_train[ii][0] for ii in range(0, len(features_train))
if labels_train[ii] == 0
]
bumpy_fast = [
features_train[ii][1] for ii in range(0, len(features_train))
if labels_train[ii] == 0
]
grade_slow = [
features_train[ii][0] for ii in range(0, len(features_train))
if labels_train[ii] == 1
]
bumpy_slow = [
features_train[ii][1] for ii in range(0, len(features_train))
if labels_train[ii] == 1
]
# You will need to complete this function imported from the ClassifyNB script.
# Be sure to change to that code tab to complete this quiz.
clf = classify(features_train, labels_train)
### draw the decision boundary with the text points overlaid
#prettyPicture(clf, features_test, labels_test)
#output_image("test.png", "png", open("test.png", "rb").read())
pred = clf.predict(features_test)
from classify import NBAccuracy
# below are the two ways accuracy can be found
print "Accuracy is: {}".format(NBAccuracy(clf, pred, labels_test))
print "Accuracy is: {}".format(clf.score(features_test, labels_test))
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData
from classify import NBAccuracy
import matplotlib.pyplot as plt
import numpy as np
import pylab as pl
features_train, labels_train, features_test, labels_test = makeTerrainData()
def submitAccuracy():
accuracy = NBAccuracy(features_train, labels_train, features_test,
labels_test)
return accuracy
if __name__ == "__main__":
print "NBGaussian accuracy:", NBAccuracy(features_train, labels_train,
features_test, labels_test)
def submitAccuracy():
accuracy = NBAccuracy(features_train, labels_train, features_test,
labels_test)
print("accuracy: ", accuracy)
return accuracy
def submitAccuracy():
accuracy = NBAccuracy(features_train, labels_train, features_test, labels_test, 2)
#return (accuracy)
print(accuracy)
