Пример #1
0
def main():
    digits = mnist() # Creates a class with our mnist images and labels
    if open('Training SVD Data','rb')._checkReadable() == 0: # Check if file exist create it if it doesn't
        print("im here")
        x = center_matrix_SVD(digits.train_Images) # Creates a class with our svd and associated info
        pickle.dump(x,open('Training SVD Data','wb'))
    else:
        x = pickle.load(open('Training SVD Data','rb'))  # If we already have the file just load it
    if 0:
        test_Images_Center = np.subtract(digits.test_Images,np.repeat(x.centers,digits.test_Images.shape[0],0))
        tic()
        labels = local_kmeans_class(x.PCA[:,:50],digits.train_Labels,[email protected](x.V[:50,:]),10)
        toc()
        pickle.dump(labels,open('Loc_kmeans_50_lab','wb'))
    loc_full = pickle.load(open('Loc_kmeans_Full_lab','rb'))
    loc_50 = pickle.load(open('Loc_kmeans_50_lab','rb'))
    labels_Full = pickle.load(open('KNN_Full','rb'))
    # Have to transpose these because they came out backwards should fix if i use this agian
    errors_full,ind_full = class_error_rate(np.transpose(loc_full),digits.test_labels)
    errors_50,ind_50 = class_error_rate(np.transpose(loc_50),digits.test_labels)
    errors_near,ind_50 = class_error_rate(labels_Full,digits.test_labels)
    plt.figure()
    plt.plot(np.arange(10)+1, errors_full, color='Green', marker='o', markersize=10, label='Full')  #plots the 82.5%
    plt.plot(np.arange(10)+1, errors_50, color='Yellow', marker='o', markersize=10, label='82.5%')
    plt.plot(np.arange(10)+1, errors_near, color='Blue', marker='o', markersize=10, label='kNN')
    plt.grid(1) # Turns the grid on
    plt.title('Plot of local KNN Error rates')
    plt.legend(loc='upper right') # Puts a legend on the plot
    plt.show()
Пример #2
0
def main():
    digits = mnist()  # Creates a class with our mnist images and labels
    if open('Training SVD Data', 'rb')._checkReadable(
    ) == 0:  # Check if file exist create it if it doesn't
        print("im here")
        x = center_matrix_SVD(
            digits.train_Images
        )  # Creates a class with our svd and associated info
        pickle.dump(x, open('Training SVD Data', 'wb'))
    else:
        x = pickle.load(open('Training SVD Data',
                             'rb'))  # If we already have the file just load it
    if 0:
        test_Images_Center = np.subtract(
            digits.test_Images,
            np.repeat(x.centers, digits.test_Images.shape[0], 0))
        tic()
        labels = local_kmeans_class(
            x.PCA[:, :50], digits.train_Labels,
            test_Images_Center @ np.transpose(x.V[:50, :]), 10)
        toc()
        pickle.dump(labels, open('Loc_kmeans_50_lab', 'wb'))
    loc_full = pickle.load(open('Loc_kmeans_Full_lab', 'rb'))
    loc_50 = pickle.load(open('Loc_kmeans_50_lab', 'rb'))
    labels_Full = pickle.load(open('KNN_Full', 'rb'))
    # Have to transpose these because they came out backwards should fix if i use this agian
    errors_full, ind_full = class_error_rate(np.transpose(loc_full),
                                             digits.test_labels)
    errors_50, ind_50 = class_error_rate(np.transpose(loc_50),
                                         digits.test_labels)
    errors_near, ind_50 = class_error_rate(labels_Full, digits.test_labels)
    plt.figure()
    plt.plot(np.arange(10) + 1,
             errors_full,
             color='Green',
             marker='o',
             markersize=10,
             label='Full')  #plots the 82.5%
    plt.plot(np.arange(10) + 1,
             errors_50,
             color='Yellow',
             marker='o',
             markersize=10,
             label='82.5%')
    plt.plot(np.arange(10) + 1,
             errors_near,
             color='Blue',
             marker='o',
             markersize=10,
             label='kNN')
    plt.grid(1)  # Turns the grid on
    plt.title('Plot of local KNN Error rates')
    plt.legend(loc='upper right')  # Puts a legend on the plot
    plt.show()
Пример #3
0
def main():
    digits = mnist() # Creates a class with our mnist images and labels
    if open('Training SVD Data','rb')._checkReadable() == 0: # Check if file exist create it if it doesn't
        x = center_matrix_SVD(digits.train_Images) # Creates a class with our svd and associated info
        pickle.dump(x,open('Training SVD Data','wb'))
    else:
        x = pickle.load(open('Training SVD Data','rb'))  # If we already have the file just load it
    if 1: # if this is zero skip
        test_Images_Center = np.subtract(digits.test_Images,np.repeat(x.centers,digits.test_Images.shape[0],0))
        tic()
        myLDA = LDA()  # Create a new instance of the LDA class
        new_train = myLDA.fit_transform(x.PCA[:,:154],digits.train_Labels)  # It will fit based on x.PCA
        new_test = myLDA.transform([email protected](x.V[:154,:])) # get my transformed test dataset
        Knn_labels = local_kmeans_class(new_train,digits.train_Labels,new_test,10) # Run kNN on the new data
        toc()
        pickle.dump(Knn_labels,open('Loc_kmeans_fda_lab','wb'))

    fda = pickle.load(open('Loc_kmeans_fda_lab','rb'))
    labels_Full = pickle.load(open('KNN_Full','rb'))
    loc_full = pickle.load(open('Loc_kmeans_Full_lab','rb'))
    errors_fda,ind_fda = class_error_rate(np.transpose(fda),digits.test_labels)
    errors_near,ind_near = class_error_rate(labels_Full,digits.test_labels)
    errors_full,ind_full = class_error_rate(np.transpose(loc_full),digits.test_labels)
    labels_50 = pickle.load(open('KNN_50','rb'))
    errors_50,ind_50 = class_error_rate(labels_50,digits.test_labels)
    print(errors_full)
    plt.figure()
    plt.plot(np.arange(10)+1, errors_fda, color='Green', marker='o', markersize=10, label='fda Kmeans')  #plots the 82.5%
    plt.plot(np.arange(10)+1, errors_near, color='Blue', marker='o', markersize=10, label='kNN')
    plt.plot(np.arange(10)+1, errors_full, color='Yellow', marker='o', markersize=10, label='Full Kmeans')
    plt.plot(np.arange(10)+1, errors_50, color='Red', marker='o', markersize=10, label='kNN 50')
    axes = plt.gca()
    axes.set_ylim([0.015,0.12])
    plt.grid(1) # Turns the grid on
    plt.title('Plot of Local Kmeans with FDA Error rates')
    plt.legend(loc='upper right')  # Puts a legend on the plot
    plt.show()
    project_back(x,digits)