Python svm示例

示例#1

train_x,train_y,test_x,test_y = train_test_split(x_all_2,y_all,1)
print(y_all)
#print(train_x)
print(train_y)

#print(test_x)
#print(test_y)
#print(x_all)
#print(y_all)
#referenceのカラーコード
#カラーコードのタグの数width=4,length=4の場合16個のタグに対応
width = 3
length = 4
#colorcode(test_y,width,length)
#SVM
best_pred=svm(train_x,train_y,test_x,test_y)
#print(best_pred)
#print('a')
#colorcode(best_pred,width,length)
#k近傍法
#best_pred=kNN(train_x,train_y,test_x,test_y)
#colorcode(best_pred,width,length)

# PCA-SVM
#transformed, targets = pCA(x_all_2, y_all)

#train_x_pca,train_y_pca,test_x_pca,test_y_pca = train_test_split(transformed,targets,1)

#best_pred = svm(train_x_pca, train_y_pca, test_x_pca, test_y_pca)
#colorcode(best_pred, width, length)

示例#2

            y_all.append(i)
        except FileNotFoundError as e:
            print(e)
#train_test_split(特徴量,目的関数,1つの厚さにおけるtrainデータの数)
train_x, train_y, test_x, test_y = train_test_split(x_all, y_all, 1)

#print(train_x)
#print(train_y)
#print(test_x)
#print(test_y)
#print(x_all)
#print(y_all)
#referenceのカラーコード
#カラーコードのタグの数width=4,length=4の場合16個のタグに対応
width = 4
length = 4
colorcode(test_y, width, length)
#SVM
best_pred = svm(train_x, train_y, test_x, test_y)
colorcode(best_pred, width, length)
#k近傍法
best_pred = kNN(train_x, train_y, test_x, test_y)
colorcode(best_pred, width, length)
# PCA-SVM
transformed, targets = pCA(x_all, y_all)

train_x_pca, train_y_pca, test_x_pca, test_y_pca = train_test_split(
    transformed, targets, 1)

best_pred = svm(train_x_pca, train_y_pca, test_x_pca, test_y_pca)
colorcode(best_pred, width, length)

示例#3

文件： One_folder_CF.py 项目： mittsusan/terahertz

    else:
        X_all = np.append(X_all, x_all, axis=1)
    l = l + 1

#print(y_list)
y_all = np.array(y_list)
#print(X_all.T)
#print(y_all.shape)

X_train, X_test, y_train, y_test = train_test_split(X_all.T,
                                                    y_all,
                                                    test_size=0.2)
#print(X_test)
print(y_test)
print('\nSVM')
best_pred = svm(X_train, y_train, X_test, y_test)
print('\nK近傍法')
best_pred = kNN(X_train, y_train, X_test, y_test)

print('\nPCA')
A, B = pCA(X_all.T, y_all)

print('\nICA')
C, D = iCA(X_all.T, y_all)

#以下測定データのラベル付け
'''
for w in sample_list:
    os.chdir('/Users/toshinari/Downloads/SVM_file/ALL_RESULT/ALL_file/Trans/{}'.format(w))
    file_list = sorted(glob.glob('/Users/toshinari/Downloads/OneDrive_1_2019-6-27/20190624/*.txt'))