from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
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
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


# store your predictions in a list named pred
clf.fit(features_train, labels_train)
pred = clf.predict(features_test)


acc = accuracy_score(pred, labels_test)  # 0.92


def submitAccuracy():
    return acc
