


'''simple docstring'''


import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
						# Initialising the CNN
						# (Sequential- Building the model layer by layer)
						lowerCamelCase__    	=     models.Sequential()

						# Step 1 - Convolution
						# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
						# (3,3) is the kernel size (filter matrix)
						classifier.add(
						    layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
						)

						# Step 2 - Pooling
						classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

						# Adding a second convolutional layer
						classifier.add(layers.ConvaD(32, (3, 3), activation='relu'))
						classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

						# Step 3 - Flattening
						classifier.add(layers.Flatten())

						# Step 4 - Full connection
						classifier.add(layers.Dense(units=128, activation='relu'))
						classifier.add(layers.Dense(units=1, activation='sigmoid'))

						# Compiling the CNN
						classifier.compile(
						    optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
						)

						# Part 2 - Fitting the CNN to the images

						# Load Trained model weights

						# from keras.models import load_model
						# regressor=load_model('cnn.h5')

						lowerCamelCase__    	=     tf.keras.preprocessing.image.ImageDataGenerator(
						    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
						)

						lowerCamelCase__    	=     tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

						lowerCamelCase__    	=     train_datagen.flow_from_directory(
						    'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
						)

						lowerCamelCase__    	=     test_datagen.flow_from_directory(
						    'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
						)

						classifier.fit_generator(
						    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
						)

						classifier.save('cnn.h5')

						# Part 3 - Making new predictions

						lowerCamelCase__    	=     tf.keras.preprocessing.image.load_img(
						    'dataset/single_prediction/image.png', target_size=(64, 64)
						)
						lowerCamelCase__    	=     tf.keras.preprocessing.image.img_to_array(test_image)
						lowerCamelCase__    	=     np.expand_dims(test_image, axis=0)
						lowerCamelCase__    	=     classifier.predict(test_image)
						# training_set.class_indices
						if result[0][0] == 0:
												lowerCamelCase__    	=     'Normal'
						if result[0][0] == 1:
												lowerCamelCase__    	=     'Abnormality detected'







'''simple docstring'''


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return math.sqrt(sum(pow(a - b      , 2   ) for a, b in zip(__lowerCAmelCase      , __lowerCAmelCase   )   )   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):

		if dataset.ndim != value_array.ndim:
				_UpperCAmelCase		:       Optional[Any]								=      (
				    "Wrong input data's dimensions... "
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(__lowerCAmelCase   )

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_UpperCAmelCase		:       Optional[int]								=      (
						    "Wrong input data's shape... "
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(__lowerCAmelCase   )
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError("Wrong shape"   )

		if dataset.dtype != value_array.dtype:
				_UpperCAmelCase		:       Union[str, Any]								=      (
				    "Input data have different datatype... "
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(__lowerCAmelCase   )

		_UpperCAmelCase		:       Optional[int]								=      []

		for value in value_array:
				_UpperCAmelCase		:       List[str]								=      euclidean(__lowerCAmelCase      , dataset[0]   )
				_UpperCAmelCase		:       Dict								=      dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_UpperCAmelCase		:       int								=      euclidean(__lowerCAmelCase      , __lowerCAmelCase   )

						if dist > temp_dist:
								_UpperCAmelCase		:       Tuple								=      temp_dist
								_UpperCAmelCase		:       Dict								=      dataset_value.tolist()

				answer.append([vector, dist]   )

		return answer






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return np.dot(__lowerCAmelCase      , __lowerCAmelCase   ) / (norm(__lowerCAmelCase   ) * norm(__lowerCAmelCase   ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



