def main():
(training_features, training_lables, test_features,
true_labels) = makeTerrainData()
classifier = classify(training_features, training_lables)
predicted_lables = classifier.predict(test_features)
simple_plot(training_features, training_lables, ['r', 'g'])
simple_plot(test_features, predicted_lables, ['y', 'b'], show=True)
print(accuracy_score(predicted_lables, true_labels))
print(calc_accuracy(true_labels, predicted_lables))
def buyselltrainer():
feature, target = csv_to_numpy_train()
from ClassifyDT import classify
clf = classify()
from sklearn.cross_validation import train_test_split
feature_train, feature_test, target_train, target_test = train_test_split(
feature, target, test_size=0.5, random_state=42)
clf.fit(feature_train, target_train)
pred = clf.predict(feature_test)
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(target_test, pred)
print(accuracy)
return clf
### and visually identify them
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)
accuracy = clf.score(features_test, labels_test)
print("the accuracy is : ", accuracy)
### 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())
#this code does not work in python 3, dont know why
#running it using the 2 line code below
image = Image.open('test.png')
image.show()
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture, output_image
#from ClassifyNB import classify
from ClassifyDT import classify
import numpy as np
import pylab as pl
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the training data (features_train, labels_train) have both "fast" and "slow" points mixed
### in together--separate them so we can give them different colors in the scatterplot,
### and visually identify them
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)
print('accuracy = ', clf.score(features_test, labels_test))
### 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())