Installing scikit-learn
# sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev gfortran
$ pip install numpy
$ pip install scipy
$ pip install scikit-learn
(source)
>>> 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)
GaussianNB()
>>> print(clf.predict([[-0.8, -1]]))
[1]
>>> clf_pf = GaussianNB()
>>> clf_pf.partial_fit(X, Y, np.unique(Y))
GaussianNB()
>>> print(clf_pf.predict([[-0.8, -1]]))
[1]import sys
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData
import matplotlib.pyplot as plt
import copy
import numpy as np
import pylab as pl
features_train, labels_train, features_test, labels_test = makeTerrainData()
########################## SVM #################################
### we handle the import statement and SVC creation for you here
from sklearn.svm import SVC
clf = SVC(kernel="linear")
#### now your job is to fit the classifier
#### using the training features/labels, and to
#### make a set of predictions on the test data
clf.fit(features_train, labels_train)
#### store your predictions in a list named pred
pred = clf.predict(features_test)
from sklearn.metrics import accuracy_score
acc = accuracy_score(pred, labels_test)
def submitAccuracy():
return acc