plt.show() return 'draw success!' def just_test(s, n1, n2): data = getData(s) label = getLabel(s) for x in range(n1, n2): print(label[x]) draw_by_pixel(data[x]) # print "train group:" # just_test('train', 5, 10) # print "test group:" # just_test('test', 105, 110) train_data ,train_lables = getData('train'),getLabel('train').squeeze() test_data ,test_lables = getData('test'),getLabel('test').squeeze() t0 =time.time() nb_clf = GaussianNB() nb_clf.fit(train_data,train_lables) nb_pred = nb_clf.predict(test_data) print("nb_clf has fitted,time cost :%.3fs"%(time.time() -t0)) print("the accuracy of Gaussian navie bayes classifier:\n",accuracy_score(test_lables, nb_pred)) from sklearn.decomposition import PCA n_components = 100 pca = PCA(svd_solver='randomized',n_components = n_components,whiten = True).fit(train_data)
def just_test(s, n1, n2): data = getData(s) label = getLabel(s) for x in range(n1, n2): print(label[x]) draw_by_pixel(data[x])
import torch import torch.nn as nn from torch.autograd import Variable import torch.nn.functional as F import torch.utils.data as Data from input_file import getData, getLabel import pdb import time t = time.time() INPUT_SIZE = 28 * 28 CLASS_NUM = 10 BATCH_SIZE = 100 EPOCH = 20 train_x = torch.from_numpy(getData("train") / 255.) train_y = torch.from_numpy(getLabel("train")) test_x = torch.from_numpy(getData("test") / 255.) test_y = torch.from_numpy(getLabel("test")) train_minist_dataset = Data.TensorDataset(data_tensor=train_x, target_tensor=train_y) test_minist_dataset = Data.TensorDataset(data_tensor=test_x, target_tensor=test_y) train_loader = Data.DataLoader( dataset=train_minist_dataset, batch_size=BATCH_SIZE, shuffle=True, ) test_loader = Data.DataLoader(