import pickle
import bayes
import imtools
# Pickleを使って2Dの点を読み込む
with open('points_normal.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# ベイズ分類器を学習させる
bc = bayes.BayesClassifier()
bc.train([class_1,class_2],[1,-1])
# Pickleを使ってテストデータを読み込む
with open('points_normal_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# いくつかの点についてテストする
print bc.classify(class_1[:10])[0]
# 点と判別境界を表示する
def classify0(x,y,bc=bc):
points = vstack((x,y))
return bc.classify(points.T)[0]
imtools.plot_2D_boundary([-6,6,-6,6],[class_1,class_2],classify0,[1,-1])
show()
from numpy import *
import pickle
import sys
sys.path.append('../ch1/')
import imtools
import bayes
# load 2D example points using Pickle
with open('points_normal.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# train Bayes classifier
bc = bayes.BayesClassifier()
bc.train([class_1,class_2],[1,-1])
# load test data using Pickle
with open('points_normal_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# test on some points
print bc.classify(class_1[:10])[0]
# plot points and decision boundary
def classify(x,y,bc=bc):
points = vstack((x,y))
return bc.classify(points.T)[0]
imtools.plot_2D_boundary([-6,6,-6,6],[class_1,class_2],classify,[1,-1])
show()
labels = pickle.load(f)
#convert to lists for libsvm
class_1 = map(list,class_1)
class_2 = map(list,class_2)
labels = list(labels)
samples = class_1+class_2
#create SVM
prob = svm_problem(labels,samples)
param = svm_parameter('-t 2')
# train SVM on data
m = svm_train(prob,param)
res = svm_predict(labels,samples,m)
with open('points_normal_test.pkl','r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
#convert to lists for libsvm
class_1 = map(list,class_1)
class_2 = map(list,class_2)
#definefunctionforplotting
def predict(x,y,model=m):
return np.array(svm_predict([0]*len(x),zip(x,y),model)[0])
#plottheclassificationboundary
imtools.plot_2D_boundary([-6,6,-6,6],[np.array(class_1),np.array(class_2)],predict,[-1,1])
show()
# 以下で、もう一方のデータ（テスト用）を読み込んで分類器をテストする
# pickleを使ってテストデータを読み込む
with open('points_normal_test.pkl','r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# いくつかの点についてテストする
print bc.classify(class_1[:10])[0]
# 点と判別境界を表示する
def classify(x,y,bc=bc):
points = np.vstack((x,y))
return bc.classify(points.T)[0]
imtools.plot_2D_boundary([-6,6,-6,6],[class_1,class_2],classify,[1,-1])
reload(pca)
#8.2.1 PCAで次元削減する
#まず前回使ったfeatures,test_featuresを読み込む
def read_gesture_features_labels(path):
# .dsiftで終わるすべてのファイル名のリストを作る
featlist = [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.dsift')]
#特徴量を読み込む
features = []
for featfile in featlist:
l,d = sift.read_features_from_file(featfile)
features.append(d.flatten())
features = np.array(features)
samples = class_1+class_2 # 2つのリストを連結する
# SVMを生成する
prob = svm_problem(labels,samples)
param = svm_parameter('-t 2')
# データを使ってSVMを学習させる
m = svm_train(prob,param)
# 学習はうまくいったかな？
res = svm_predict(labels,samples,m)
# Pickleを用いてテストデータを読み込む
with open('points_ring_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# libsvm用にリストに変換する
class_1 = map(list,class_1)
class_2 = map(list,class_2)
# 描画用の関数を定義する
def predict(x,y,model=m):
return array(svm_predict([0]*len(x),zip(x,y),model)[0])
# 分類境界を描画する
imtools.plot_2D_boundary([-6,6,-6,6],[array(class_1),array(class_2)],
predict,[1,-1])
show()
labels = list(labels)
samples = class_1 + class_2 # concatenate the two lists
# create SVM
prob = svm_problem(labels, samples)
param = svm_parameter('-t 2')
# train SVM on data
m = svm_train(prob, param)
# how did the training do?
res = svm_predict(labels, samples, m)
# load test data using Pickle
with open('points_normal_test.pkl', 'r') as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# convert to lists for libsvm
class_1 = map(list, class_1)
class_2 = map(list, class_2)
# define function for plotting
def predict(x, y, model=m):
return array(svm_predict([0] * len(x), map(list, zip(x, y)), model)[0])
# plot the classification boundary
imtools.plot_2D_boundary([-6, 6, -6, 6],
[array(class_1), array(class_2)], predict, [-1, 1])
show()