def test():
import numpy as np
x = np.array([[-1,1], [-2, 1], [-3, -2], [1, 1],[2, 1],[3, 2]])
y = np.array([1, 1, 1, 2, 2, 2])
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(x,y)
print(clf.predict([-0.8, -1]))
from prep_terrain_data import makeTerrainData
a = makeTerrainData()
print len(a[3])
def classify(features_train, labels_train):
### import the sklearn module for GaussianNB
### create classifier
### fit the classifier on the training features and labels
### return the fit classifier
### your code goes here!
from sklearn.naive_bayes import GaussianNB
from prep_terrain_data import makeTerrainData
a = makeTerrainData()
clf = GaussianNB()
clf.fit(a[0],a[1])
return clf
#!/usr/bin/python
import matplotlib.pyplot as plt
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture
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]
#### initial visualization
plt.xlim(0.0, 1.0)
plt.ylim(0.0, 1.0)
plt.scatter(bumpy_fast, grade_fast, color = "b", label="fast")
plt.scatter(grade_slow, bumpy_slow, color = "r", label="slow")
plt.legend()
plt.xlabel("bumpiness")
plt.ylabel("grade")
plt.show()
#################################################################################
### your code here! name your classifier object clf if you want the
### visualization code (prettyPicture) to show you the decision boundary
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
"Random Forest", "AdaBoost", "Naive Bayes", "Linear Discriminant Analysis",
"Quadratic Discriminant Analysis"]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
AdaBoostClassifier(),
GaussianNB(),
LinearDiscriminantAnalysis(),
QuadraticDiscriminantAnalysis()]
X, y ,a,b=makeTerrainData()
datasets =[ [X, y ] ]
figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds in datasets:
# preprocess dataset, split into training and test part
X, y=ds
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
#!/usr/bin/python
from __future__ import print_function
import matplotlib.pyplot as plt
from time import time
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the training data (features_train, labels_train) have both "fast" and "slow"
### points mixed together--separate them so we can give them different colors
### in the scatterplot and identify them visually
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]
#### initial visualization
plt.xlim(0.0, 1.0)
plt.ylim(0.0, 1.0)
plt.scatter(bumpy_fast, grade_fast, color = "b", label="fast")
plt.scatter(grade_slow, bumpy_slow, color = "r", label="slow")
plt.legend()
plt.xlabel("bumpiness")
plt.ylabel("grade")
plt.show()
################################################################################
def __init__(self, N_points):
self.features_train, self.labels_train, self.features_test, self.labels_test = makeTerrainData(N_points)
import numpy as np from prep_terrain_data import makeTerrainData from matplotlib import pyplot as plt from sklearn import svm import pandas from class_vis import prettyPicture x_train, y_train, x_test, y_test = makeTerrainData() df = pandas.DataFrame(data=x_test, columns=['f_1', 'f_2']) df['label'] = pandas.Series(y_test) dat_pos = df[df.label == 1] dat_neg = df[df.label == 0] plt.scatter(dat_pos['f_1'], dat_pos['f_2'], c='blue') plt.scatter(dat_neg['f_1'], dat_neg['f_2'], c='red') #plt.show() clf = svm.SVC(kernel='rbf', C=0.01) clf.fit(x_train, y_train) prettyPicture(clf, x_test, y_test) #w = clf.coef_[0] #a = -w[0]/w[1] #m = clf.intercept_[0] / w[1] #x_ = np.linspace(0.0,1.0) #y_ = a*x_ - m #plt.plot(x_,y_)
import numpy as np from prep_terrain_data import makeTerrainData from matplotlib import pyplot as plt from sklearn import svm import pandas from class_vis import prettyPicture x_train,y_train,x_test,y_test = makeTerrainData() df = pandas.DataFrame(data=x_test,columns=['f_1','f_2']) df['label'] = pandas.Series(y_test) dat_pos = df[df.label==1] dat_neg = df[df.label==0] plt.scatter(dat_pos['f_1'],dat_pos['f_2'],c='blue') plt.scatter(dat_neg['f_1'],dat_neg['f_2'],c='red') #plt.show() clf = svm.SVC(kernel='rbf',C=0.01) clf.fit(x_train,y_train) prettyPicture(clf,x_test,y_test) #w = clf.coef_[0] #a = -w[0]/w[1] #m = clf.intercept_[0] / w[1] #x_ = np.linspace(0.0,1.0) #y_ = a*x_ - m
def classify(features_train, labels_train):
### import the sklearn module for GaussianNB
### create classifier
### fit the classifier on the training features and labels
### return the fit classifier
### your code goes here!
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)
### 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())
