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()
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()
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)
