and the second entry is the average of the bottom half of the image
= rows floor(m/2) [inclusive] to m
"""
n_samples = len(x)
m = len(x[0])
n = len(x[0, 0])
cut = int(np.floor(m / 2))
result = []
for i in range(n_samples):
top_half = x[i, :cut, :]
bottom_half = x[i, cut:, :]
tb = np.array([np.mean(top_half), np.mean(bottom_half)])
result.append(tb)
result = np.array(result)
return result.T
# use this function to evaluate accuracy
acc_row = hw3.get_classification_accuracy(row_average_features(data), labels)
acc_col = hw3.get_classification_accuracy(col_average_features(data), labels)
acc_tb = hw3.get_classification_accuracy(top_bottom_features(data), labels)
print("row feature accuracy is {}, column accuracy is {}, \
top/bottom accuracy is {} ".format(acc_row, acc_col, acc_tb))
#-------------------------------------------------------------------------------
# Analyze MNIST data
#-------------------------------------------------------------------------------
# Your code here to process the MNIST data
raise Exception("modify me!")
def col_average_features(x):
"""
@param x (n_samples,m,n) array with values in (0,1)
@return (n,n_samples) array where each entry is the average of a column
"""
raise Exception("modify me!")
def top_bottom_features(x):
"""
@param x (n_samples,m,n) array with values in (0,1)
@return (2,n_samples) array where the first entry of each column is the average of the
top half of the image = rows 0 to floor(m/2) [exclusive]
and the second entry is the average of the bottom half of the image
= rows floor(m/2) [inclusive] to m
"""
raise Exception("modify me!")
# use this function to evaluate accuracy
acc = hw3.get_classification_accuracy(raw_mnist_features(data), labels)
print(acc)
#-------------------------------------------------------------------------------
# Analyze MNIST data
#-------------------------------------------------------------------------------
# Your code here to process the MNIST data
#add the caculated features to the data set
row_feature_set = np.concatenate((row_feature_set, row_feat), axis=1)
col_feature_set = np.concatenate((col_feature_set, col_feat), axis=1)
top_bot_feature_set = np.concatenate(
(top_bot_feature_set, top_bot_feat), axis=1)
return row_feature_set, col_feature_set, top_bot_feature_set
flat = raw_mnist_features(data)
new_features = extracted_feature_set(data)
print(new_features.shape)
print(labels.shape)
# use this function to evaluate accuracy
# acc = hw3.get_classification_accuracy(raw_mnist_features(data), labels)
acc = hw3.get_classification_accuracy(extracted_feature_set(data), labels)
print('combined feature accuracy:', acc)
#part 6.2 evaluations
#generate the feature sets
row_feature_set, col_feature_set, top_bot_feature_set = iterate_individual_feature(
data)
#evaluate the accuracy of each feature set by calling get_classification_accuracy
for feat in (row_feature_set, col_feature_set, top_bot_feature_set):
acc = hw3.get_classification_accuracy(feat, labels)
print(f'accuracy for feature set is : {acc}')
#-------------------------------------------------------------------------------
# Analyze MNIST data
#-------------------------------------------------------------------------------
# Your code here to process the MNIST data