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- 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)
- print pred
- from sklearn.metrics import accuracy_score
- acc = accuracy_score(pred, labels_test)
- def submitAccuracy():
- return acc
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