Python limited_input1 예제들

예제 #1

        filename for filename in os.listdir(training_output)
        if filename.endswith('.fisher.npz')
    ]
    testing = [
        filename for filename in os.listdir(testing_output)
        if filename.endswith('.fisher.npz')
    ]

    training_dict = classify_library.toDict(training)
    testing_dict = classify_library.toDict(testing)

    ####################################################################
    ####################################################################
    ################################## Script starts

    X_train_vids = classify_library.limited_input1(training_dict, 1000)
    X_test_vids = classify_library.limited_input1(testing_dict, 1000)

    #GET THE TRAINING AND TESTING DATA.
    X_train, Y_train = classify_library.make_FV_matrix(X_train_vids,
                                                       training_output,
                                                       class_index)
    X_test, Y_test = classify_library.make_FV_matrix(X_test_vids,
                                                     testing_output,
                                                     class_index)

    X_total = np.concatenate((X_train, X_test), 0)
    Y_total = np.concatenate((Y_train, Y_test), 0)

    if not args.no_pca:
        #PCA reduction

예제 #2

파일: classify_experiment.py 프로젝트: zhuxinqimac/CS221_Project

    if filename.endswith('.fisher.npz')
]
testing = [
    filename for filename in os.listdir(testing_output)
    if filename.endswith('.fisher.npz')
]

print(training[:5])
print(testing[:5])
print(train_vid_class.keys()[:5])
training_dict = classify_library.toDict(training, train_vid_class)
testing_dict = classify_library.toDict(testing, test_vid_class)

#GET THE TRAINING AND TESTING DATA.

X_train_vids = classify_library.limited_input1(training_dict,
                                               args.per_class_num)
X_test_vids = classify_library.limited_input1(testing_dict, args.per_class_num)
# X_train_vids, X_test_vids = classify_library.limited_input(training_dict, testing_dict, 101, 24)
X_train, Y_train = classify_library.make_FV_matrix(X_train_vids,
                                                   training_output,
                                                   class_index,
                                                   train_vid_class)
X_test, Y_test = classify_library.make_FV_matrix(X_test_vids, testing_output,
                                                 class_index, test_vid_class)

# pdb.set_trace()

training_PCA = classify_library.limited_input1(training_dict, 1)

if not args.PCA_dim:
    X_train_PCA = X_train.tolist()

예제 #3

class_index_file = "./class_index.npz"
class_index_file_loaded = np.load(class_index_file)
class_index = class_index_file_loaded['class_index'][()]
index_class = class_index_file_loaded['index_class'][()]

training = [
    filename for filename in os.listdir(training_output)
    if filename.endswith('.jpeg')
]
testing = [
    filename for filename in os.listdir(testing_output)
    if filename.endswith('.jpeg')
]

training_dict = classify_library.toDict(training)
training_PCA = classify_library.limited_input1(training_dict, 1)

X_train, Y_train = make_frame_matrix(training, training_output, class_index)
X_test, Y_test = make_frame_matrix(testing, testing_output, class_index)

### Reduced PCA dimension to 1000

# In[18]:

X_PCA, _ = make_frame_matrix(training_PCA, training_output, class_index)
pca = PCA(n_components=1000)
pca.fit(X_PCA)
X_train_PCA = pca.transform(X_train)
X_test_PCA = pca.transform(X_test)

# In[22]:

예제 #4

파일: train_classifier.py 프로젝트: VeyronWang/CS221_Project

training = [filename for filename in os.listdir(training_output) if filename.endswith('.fisher.npz')]
testing = [filename for filename in os.listdir(testing_output) if filename.endswith('.fisher.npz')]


training_dict = classify_library.toDict(training)
testing_dict = classify_library.toDict(testing)

####################################################################
####################################################################
################################## Script starts




X_train_vids = classify_library.limited_input1(training_dict, 1000)
X_test_vids = classify_library.limited_input1(testing_dict, 1000)


#GET THE TRAINING AND TESTING DATA.
X_train, Y_train = classify_library.make_FV_matrix(X_train_vids,training_output, class_index)
X_test, Y_test = classify_library.make_FV_matrix(X_test_vids,testing_output, class_index)

#PCA reduction
training_PCA = classify_library.limited_input1(training_dict,40)
X_PCA, _ = classify_library.make_FV_matrix(training_PCA,training_output, class_index)

n_components = 1000
pca = PCA(n_components=n_components)
pca.fit(X_PCA)
X_train_PCA = pca.transform(X_train)

예제 #5

파일: baseline.py 프로젝트: VeyronWang/CS221_Project

    return (X,Y)



# In[16]:

class_index_file = "./class_index.npz"
class_index_file_loaded = np.load(class_index_file)
class_index = class_index_file_loaded['class_index'][()]
index_class = class_index_file_loaded['index_class'][()]

training = [filename for filename in os.listdir(training_output) if filename.endswith('.jpeg')]
testing = [filename for filename in os.listdir(testing_output) if filename.endswith('.jpeg')]

training_dict = classify_library.toDict(training)
training_PCA = classify_library.limited_input1(training_dict,1)

X_train, Y_train = make_frame_matrix(training,training_output,class_index)
X_test, Y_test = make_frame_matrix(testing,testing_output,class_index)


### Reduced PCA dimension to 1000

# In[18]:

X_PCA, _ = make_frame_matrix(training_PCA, training_output, class_index)
pca = PCA(n_components=1000)
pca.fit(X_PCA)
X_train_PCA = pca.transform(X_train)
X_test_PCA = pca.transform(X_test)