convolution OK!

#Navod na pouziti, Mgr. Hynek Mlčoušek, v Brne 2.5.2024
#Ulozte do lokalniho souboru u sebe na PC data tohoto tvaru vzdy ukoncene 0 ci 1 (jde o uceni s ucitelem: 1 = nemocny, 0 = prezil/zdravy, ve vystupu bude zelena znacit 0, cervena 1)  a bez znaku #; pozor na ","

# [ [23.657800719276743,18.859916797201468,0],
# [22.573729142097473,17.96922325097786,0],
# [32.55342396968757,29.463651408558803,0],
# [6.718035041529263,25.704665468161718,1],
# [14.401918566243225,16.770856492924658,0],
# [17.457907312962234,21.76521470574044,0],
# [20.02796946568093,73.45445954770891,1],
# [30.295138369778076,62.901112886193246,1],
# [15.128977804449633,32.40267702110393,0],
# [30.179457395820013,58.982492125646104,1],
# [28.01649701854089,63.92781357637711,1],
# [16.791838457871147,42.33482314089884,0],
# [10.583694293380976,19.61926728942497,0],
# [26.634447074406467,91.96624817360987,1],
# [26.217868623367643,36.400293587062976,0],
# [17.689396788624936,60.79797114006423,1],
# [33.17193822527976,66.75277364959176,1],
# [23.793952755709153,22.57501437360518,0]]

#kliknete na cerne tlacitko s trojuhelnickem vlevo nahore
#pod kodem se objevi moznost spustit dialogove okenko, kliknete na nej
#soubor, ktery mate z bodu vyse vyberte a nahrajte
#Najdete v tomto kodu retezec:
###ZDE VLOZTE DATA OD NOVYCH PACIENTU

#Vlozte do pole
# new_persons_results = []
# data o nekolika malo novych pacientech bez ukoncovaci 0 a 1, ale se stejnym poctem sloupcu jako ma soubor z Vaseho lokalniho disku, vyse by tedy toto bylo rovno 2
#kod vyhodi hned po natrenovani, (jehoz prubeh muzete sledovat na modre progres bare) pro kazdy radek z new_persons_results bilo-sedo-cerne ctverecky vznikle z normalizace poskytnutych dat a ukoncovaci ctverecek cerveny pripadne zeleny
#zaroven s tim se vypise realne cislo mezi 0 a 1 znacici jak moc je pacient zdravy (blizke 0) ci nemocny (blizke 1)
#cisla uprostred pak indikuji zadany oranzovy semafor.
#je na lekarich nastavit tresholdy (tedy pravdepodobnosti: cisla mezi 0 a 1) ktere pak daji zaver, zda je pacient cerveny, oranzovy ci zeleny

# prosim o komnetare a vysledky na realnych datech, je zadouci aby radku v matici, tedy pacientu byly stovky a sloupcu desitky
# Moznosti vyuziti: onkologicka diagnoza vs. zdrava kontorlni skupina, diabetes (pritomnost/nepritomnost), testovani noveho leku oproti placebu atd.

#kod zaroven vyhodi confusion matici, tedy mozne True Negative a False Positive plus spravne zarazene hodnoty spolu s presnosti,F1 score recall atd.
#poznamka ke kodu: jde o epxerimentalni verzi, ktera krome skutecne potrebneho kodu obsahuje ladici informace, ruzne duplicity, nadbytecne prikazy atd.
# Na uvod behu programu se pro kontorlu vypise poskytnuta matice a jeji normalizovana verze, je treba sjet jezdcem napravo nize na obrazky a dalsi vystupy

#Dekuji profesoru Petru Dostalovi za namet k teto praci a poskytnuta data, byt je potreba mit data realna

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tqdm import tqdm


from IPython.display import display
from IPython.display import Javascript
display(Javascript('IPython.OutputArea.auto_scroll_threshold = 9999;'))

label_colors = {0: [0, 128, 0], 1: [255, 0, 0]}
label_colors_testing = {0: [0, 128, 0], 1: [255, 0, 0]}


%matplotlib inline



# Function to create images based on predictions
def create_image(data, predictions):
    num_rows, num_columns = len(data), len(data[0])
    image = np.zeros((num_rows, num_columns + 1, 3), dtype=np.uint8)

    for i in range(num_rows):
        for j in range(num_columns):
            pixel_value = int(np.interp(data[i][j], [np.min(data), np.max(data)], [0, 255]))
            image[i, j] = np.array([pixel_value] * 3)

        # Create a gradient based on the normalized values
        gradient_value = int(np.interp(predictions[i], [0, 1], [0, 255]))
        image[i, -1] = np.array([gradient_value] * 3)

    return image

def create_image(data, predictions):
    num_rows, num_columns = len(data), len(data[0])
    image = np.zeros((num_rows, num_columns + 1, 3), dtype=np.uint8)

    for i in range(num_rows):
        for j in range(num_columns):
            pixel_value = int(np.interp(data[i][j], [np.min(data), np.max(data)], [0, 255]))
            image[i, j] = np.array([pixel_value] * 3)

        # Use red for class 0 and green for class 1
        if predictions[i] == 0:
            image[i, -1] = np.array([255, 0, 0])  # Red
        elif predictions[i] == 1:
            image[i, -1] = np.array([0, 128, 0])  # Green

    return image

def create_image(data, predictions, label_colors):
    num_rows, num_columns = len(data), len(data[0])
    image = np.zeros((num_rows, num_columns + 1, 3), dtype=np.uint8)

    for i in range(num_rows):
        for j in range(num_columns):
            pixel_value = int(np.interp(data[i][j], [np.min(data), np.max(data)], [0, 255]))
            image[i, j] = np.array([pixel_value] * 3)

        # Use the specified color for the last column based on the label
        image[i, -1] = label_colors[predictions[i]]

    return image



def create_imageN(data, predictions, label_colors=None):
    num_training_rows = len(data)  # Set the number of rows based on the data
    num_columns = len(data[0])

    image_training = np.zeros((num_training_rows, num_columns + 1, 3), dtype=np.uint8)

    min_pixel_value = np.min(X_train_normalized)
    max_pixel_value = np.max(X_train_normalized)




    for i in range(num_training_rows):
        # Normalize the first columns independently
        for j in range(num_columns):
            pixel_value = int(np.interp(data[i][j], [min_pixel_value, max_pixel_value], [0, 255]))
            image_training[i, j] = np.array([pixel_value] * 3)

        # Normalize the last column separately to achieve grayscale
        pixel_value_last = int(np.interp(data[i][-1], [min_pixel_value, max_pixel_value], [0, 255]))
        image_training[i, -1] = np.array([pixel_value_last] * 3)

        # Use the specified color for the last column based on the label
        if label_colors is not None:
            image_training[i, -1] = label_colors[predictions[i]]

    return image_training




# Load data from a file
#file_path = 'C:/Users/Hynek/Desktop/example4.txt'
from google.colab import files
uploaded = files.upload()

# Tento kód otevře dialogové okno pro výběr souboru z vašeho počítače.
import io
import pandas as pd

# Předpokládáme, že jste nahráli CSV soubor
for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))
  path = io.BytesIO(uploaded[fn])  # Pro soubory, které potřebují být čteny jako binární objekty
  df = pd.read_csv(path)
  print(df.head())  # Vypíše prvních pět řádků DataFrame


all_results = []
#with open(file_path, 'r') as file:
#    file_content = file.read()

# Execute the content as Python code
##exec(file_content)

import os
import shutil
import ast

for filename in uploaded.keys():
    original_path = f"/content/{filename}"
    destination_path = os.path.join("/content/", "/content/DATA2")
    shutil.move(original_path, destination_path)
    print(f"Soubor {filename} byl přesunut do {destination_path}")

file_path = '/content/DATA2'  # Cesta k souboru
with open(file_path, 'r') as file:
    code = file.read()

A_list = ast.literal_eval(code)


# Převod na NumPy pole
A = np.array(A_list)

#exec(code)

# Now, all_results contains lists corresponding to each row in the CSV file
##print(all_results)

# Assign values to variables dynamically based on the rows of matrix A
for i, row in enumerate(A, start=1):
    globals()[f"person{i}_results"] = list(row)

# Print the assigned variables
for i in range(1, len(A) + 1):
  #  print(f"person{i}_results {globals()[f'person{i}_results']}")
    all_results.append(f"person{i}_results")
##print(all_results)



result_variables = []

# Loop through the variable names and get the corresponding variables using globals()
for var_name in all_results:
    result_variables.append(globals()[var_name])

# Now, result_variables contains the variables with names specified in variable_names
#print(result_variables)

all_results = result_variables
new_persons_results = result_variables



labels = [results[-1] for results in all_results]

# Odstranění posledního sloupce z datasetu
data = [results[:-1] for results in all_results]

# Definice počtu řádků pro trénování a testování
num_training_rows = 50
num_testing_rows = 50

# Rozdělení datasetu na trénovací a testovací sady
X_train, X_test, y_train, y_test = data[:num_training_rows], data[:num_testing_rows], labels[:num_training_rows], labels[:num_testing_rows]

# Převod na NumPy pole
X_train = np.array(X_train)
X_test = np.array(X_test)
y_train = np.array(y_train)
y_test = np.array(y_test)

# # Normalizace dat (s ohledem na -1)
# min_values = np.min(X_train[X_train != -1], axis=0)
# max_values = np.max(X_train[X_train != -1], axis=0)
# X_train_normalized = (X_train - min_values) / (max_values - min_values)



# import numpy as np
# import tensorflow as tf
# import matplotlib.pyplot as plt
# from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
# import seaborn as sns
# from tqdm.notebook import tqdm_notebook



import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tqdm.notebook import tqdm_notebook

# Průměry (mu) a směrodatné odchylky (sigma)
mu = np.mean(X_train, axis=0)
sigma = np.std(X_train, axis=0)

# Normalizace každého sloupce zvlášť
X_train_standardized = (X_train - mu) / sigma
X_test_standardized = (X_test - mu) / sigma

# Vylepšený model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(256, activation='relu', input_shape=(len(X_train[0]),)),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Použití Adam optimizer s learning rate schedulerem
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate=1e-3,
    decay_steps=10000,
    decay_rate=0.9
)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

# Kompilace modelu
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])

# Lists to store accuracy values
accuracy_history = []

# Create images for the training data
image_training = np.zeros((num_training_rows, len(X_train[0]) + 1, 3), dtype=np.uint8)

min_pixel_value = np.min(X_train_standardized, axis=0)
max_pixel_value = np.max(X_train_standardized, axis=0)

for i, label in enumerate(y_train):
    for j in range(len(X_train_standardized[0])):
        pixel_value = int(np.interp(X_train_standardized[i][j], [min_pixel_value[j], max_pixel_value[j]], [0, 255]))
        image_training[i, j] = np.array([pixel_value] * 3)
    image_training[i, -1] = np.array([128, 128, 128])
    if label == 0:
        image_training[i, -1] = np.array([0, 128, 0])
    elif label == 1:
        image_training[i, -1] = np.array([255, 0, 0])

# Training the model
epochs = 139
new_persons_results = [
    # [23.65780072, 18.8599168],
    # [22.57372914, 17.96922325],
    # [32.55342397, 29.46365141],
    # [ 6.71803504, 25.70466547],
    # [14.40191857, 16.77085649],
    # [17.45790731, 21.76521471],
    # [20.02796947, 73.45445955],
    # [26.2042, 10.6782],
    # [35.7258, 12.8027],
    # [21.2, 7.8],
    # [50.1, 40.2],
    # [32.739, 42.0152],
    # [28.1, 10.1],
    # [32.1656, 46.005 ],
    # [24.9647, 51.3433],
    # [34.6134, 44.4012],
[0.0697400418162155,0.048866857264291144,0.28641370855472326,0.2721997143501177],
[0.14159602676789837,0.1747877034447084,0.35616475477076587,0.3349487498168958],
[0.11173253224821383,0.18794447828677996,0.3254176976987727,0.3413023918178341],
[0.09630381764770453,0.05449810810962146,0.26767869268577593,0.21134056616439179],
[0.17834821693532132,0.18466538062866059,0.3199711146234129,0.3968137366419059],
[0.06045619825051427,0.05598696779492471,0.21592696351263593,0.22040624440430515],
[0.08666288081774745,0.015388075894536557,0.2041876616268118,0.20706370434663773],
[0.03130184508345673,0.015266595360551428,0.27183777103946916,0.2867664339707584],
[0.05547626859495597,0.05808291988099526,0.2542166524648567,0.2573313511422864],
[0.1772, 0.0076, 0.3565, 0.2584],
]

import sys

for epoch in tqdm_notebook(range(epochs)):
    history = model.fit(X_train_standardized, np.array(y_train), epochs=1, verbose=0, shuffle=False)
    accuracy_history.append(history.history['accuracy'][0])

    if epoch == 1:
        # Normalize the testing data
        X_test_standardized = (X_test - mu) / sigma
        y_pred_after_2nd_epoch = model.predict(X_test_standardized)
        y_pred_binary_after_2nd_epoch = [1 if pred >= 0.5 else 0 for pred in y_pred_after_2nd_epoch]
        image_testing_before_2nd_epoch = create_image(X_test_standardized, y_pred_binary_after_2nd_epoch, label_colors_testing)

    if epoch >= epochs-1:
        print(f"HERE HERE Epoch: {epoch}, Epochs: {epochs}\n")
        sys.stdout.flush()

        # Iterate through new persons
        for idx, personNEW_results in enumerate(new_persons_results, start=0):
            # Ensure that personNEW_results has the same number of features as the model expects
            assert len(personNEW_results) == len(X_train[0]), "Mismatch in the number of features."

            personNEW_results_standardized = (np.array(personNEW_results) - mu) / sigma

            personNEW_prediction = model.predict(np.array([personNEW_results_standardized]))
            personNEW_label = 1 if personNEW_prediction >= 0.5 else 0
            y_pred_after_50_epochs = model.predict(X_test_standardized)
            y_pred_binary_after_50_epochs = [1 if pred >= 0.5 else 0 for pred in y_pred_after_50_epochs]
            image_testing_after_50_epochs = create_image(X_test_standardized, y_pred_binary_after_50_epochs, label_colors_testing)

            # Create an image for the new person
            image_personNEW = create_imageN([personNEW_results_standardized], [personNEW_label], label_colors)

            # Display the images
            plt.figure(figsize=(5, 5))
            plt.imshow(image_personNEW)
            plt.title(f"New Person {idx}\nLabel: {personNEW_label}, Prediction: {personNEW_prediction}, personNEW_results: {personNEW_results}")
            plt.axis("off")
            plt.show()

# Display the images
plt.figure(figsize=(25, 15))
plt.subplot(2, 2, 1)
plt.imshow(image_training)
plt.title("Training Data")
plt.axis("off")

plt.subplot(2, 2, 2)
plt.imshow(image_testing_before_2nd_epoch)
plt.title("Testing Data (2nd Epoch)")
plt.axis("off")

plt.subplot(2, 2, 3)
plt.imshow(image_testing_after_50_epochs)
plt.title(f"Testing Data ({epochs} Epochs)")
plt.axis("off")

plt.subplot(2, 2, 4)
plt.imshow(image_personNEW)
plt.title(f"New Person\nLabel: {personNEW_label},[{personNEW_prediction}]")
plt.axis("off")

# Plot accuracy history
plt.figure(figsize=(12, 5))
plt.plot(range(1, epochs + 1), accuracy_history, marker='o')
plt.title('Accuracy Over Epochs')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.grid()

# Print standardized data
print("Standardized PersonNEW Data:")
print(personNEW_results_standardized)

plt.show()

print("X_train before standardization:")
print(X_train)
print("X_test before standardization:")
print(X_test)

import seaborn as sns

print("KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK")
print(X_test)
print("HHHHHHHHHHHHHHHHHHHHHHHHHHHHHH")
print(X_train)
print("LLLLLLLLLLLLLLLLLLLLLLLLLLLLL")

# y_pred_binary = [1 if pred >= 0.5 else 0 for pred in model.predict(X_test_standardized)]

# # Create confusion matrix
# conf_matrix = confusion_matrix(y_train, y_pred_binary)
# print(conf_matrix)

from sklearn.metrics import confusion_matrix
from tensorflow.keras.utils import to_categorical

np.set_printoptions(threshold=np.inf, precision=4, suppress=True)

# Assuming X_test_standardized and y_test are your test set data
y_pred_binary = [1 if pred >= 0.5 else 0 for pred in model.predict(X_test_standardized)]

# Create confusion matrix using the test set
conf_matrix = confusion_matrix(y_test, y_pred_binary)
print(conf_matrix)

plt.figure(figsize=(6, 6))
sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=['Predicted 0', 'Predicted 1'], yticklabels=['Actual 0', 'Actual 1'])
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()

X_train = np.array(X_train)
#y_train_one_hot = np.array(y_train_one_hot)

# Rozdělení dat na trénovací a testovací množiny
X_train, X_test, y_train, y_test = data[:num_training_rows], data[:num_testing_rows], labels[:num_training_rows], labels[:num_testing_rows]

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
import tensorflow as tf
import seaborn as sns

# Assuming data splitting and model definition have been done correctly

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
print("Training Start")
for epoch in tqdm_notebook(range(100), desc="Training Progress"):
    model.fit(np.array(X_train_standardized), np.array(y_train), epochs=1, verbose=0)
print("Training Complete")

# Generate predictions from the model
predictions = (model.predict(X_test_standardized) > 0.5).astype(int)

# Convert y_test to a numpy array and then to binary labels
y_test_array = np.array(y_test)  # Convert y_test to a numpy array
y_test_binary = (y_test_array > 0.5).astype(int)  # Convert to binary

# Compute the confusion matrix
conf_matrix = confusion_matrix(y_test_binary, predictions)

# Evaluate the model's performance
accuracy = accuracy_score(y_test_binary, predictions)
precision = precision_score(y_test_binary, predictions)
recall = recall_score(y_test_binary, predictions)
f1 = f1_score(y_test_binary, predictions)

# Display the confusion matrix
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

print(f"Accuracy: {accuracy:.4f}")
print(f"Precision: {precision:.4f}")
print(f"Recall: {recall:.4f}")
print(f"F1 Score: {f1:.4f}")

print(f"Confusion Matrix2122:\n{conf_matrix}")

import random

def find_best_pair(min_val, max_val, num_features, model, min_values, max_values):
    best_pair = None
    best_prediction = 1
    for _ in range(1000):  # Number of iterations to find the best pair
        new_data = np.random.uniform(min_val, max_val, num_features)
        new_data_standardized = (new_data - mu) / sigma

        # Suppress model output
        tf.get_logger().setLevel('ERROR')
        with tf.device('/CPU:0'):  # Ensure to run on CPU to minimize unwanted logs
            prediction = model.predict(np.array([new_data_standardized]), verbose=0)[0][0]
        tf.get_logger().setLevel('INFO')

        if prediction < best_prediction:
            best_prediction = prediction
            best_pair = new_data
    return best_pair, best_prediction

best_pair, best_prediction = find_best_pair(min_values, max_values, len(X_train[0]), model, min_values, max_values)

def find_worst_pair(min_val, max_val, num_features, model, min_values, max_values):
    worst_pair = None
    worst_prediction = 0
    for _ in range(1000):  # Number of iterations to find the best pair
        new_data = np.random.uniform(min_val, max_val, num_features)
        new_data_standardized = (new_data - mu) / sigma

        # Suppress model output
        tf.get_logger().setLevel('ERROR')
        with tf.device('/CPU:0'):  # Ensure to run on CPU to minimize unwanted logs
            prediction = model.predict(np.array([new_data_standardized]), verbose=0)[0][0]
        tf.get_logger().setLevel('INFO')

        if prediction > worst_prediction:
            worst_prediction = prediction
            worst_pair = new_data
    return worst_pair, worst_prediction

worst_pair, worst_prediction = find_worst_pair(min_values, max_values, len(X_train[0]), model, min_values, max_values)

print(f"Best Pair: {best_pair}, Best Prediction: {best_prediction}")
print(f"Worst Pair: {worst_pair}, Worst Prediction: {worst_prediction}")



import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.metrics import recall_score, confusion_matrix, accuracy_score, precision_score, f1_score
import seaborn as sns
from tqdm.notebook import tqdm_notebook
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA



# Rozdělení na vstupní data (X) a cílové proměnné (y)
X = A[:, :-1]
y = A[:, -1]

# Rozdělení na trénovací a testovací sadu (v tomto příkladě použijeme celou sadu jako trénovací pro jednoduchost)
X_train, y_train = X, y
X_test, y_test = X, y

# Výpočet průměru a směrodatné odchylky pro každý sloupec
mu = np.mean(X_train, axis=0)
sigma = np.std(X_train, axis=0)

# Normalizace každého sloupce zvlášť
X_train_standardized = (X_train - mu) / sigma

# Normalizace testovacích dat
X_test_standardized = (X_test - mu) / sigma

# Definice modelu
model = tf.keras.Sequential([
    tf.keras.layers.Dense(256, activation='relu', input_shape=(X_train_standardized.shape[1],)),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Použití Adam optimizer s learning rate schedulerem
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate=1e-3,
    decay_steps=10000,
    decay_rate=0.9
)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

# Kompilace modelu
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy', tf.keras.metrics.Recall()])

# Trénování modelu
history = model.fit(X_train_standardized, y_train, epochs=50, verbose=0, shuffle=False)

# Predikce
y_pred_prob = model.predict(X_test_standardized)
y_pred = (y_pred_prob > 0.5).astype(int)

# Výpočet metrik
recall = recall_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)

# Vyhodnocení výkonu modelu
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

# Výpis metrik
print(f"Recall: {recall:.4f}")
print(f"Accuracy: {accuracy:.4f}")
print(f"Precision: {precision:.4f}")
print(f"F1 Score: {f1:.4f}")
print(f"Confusion Matrix:\n{conf_matrix}")

# Zobrazení confusion matrix
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

print("KKKKKKKKKKKKK")



# import numpy as np
# import tensorflow as tf
# import matplotlib.pyplot as plt
# from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
# import seaborn as sns
# from tqdm.notebook import tqdm_notebook

# # Výpočet průměru a směrodatné odchylky pro každý sloupec
# mu = np.mean(X_train, axis=0)
# sigma = np.std(X_train, axis=0)

# # Normalizace každého sloupce zvlášť
# X_train_standardized = (X_train - mu) / sigma

# # Normalizace testovacích dat
# X_test_standardized = (X_test - mu) / sigma

# # Převedení dat na správný tvar pro CNN (přidání kanálu)
# X_train_standardized = X_train_standardized[..., np.newaxis]
# X_test_standardized = X_test_standardized[..., np.newaxis]

# # Definice modelu
# model = tf.keras.Sequential([
#     tf.keras.layers.Conv1D(32, 2, activation='relu', input_shape=(X_train_standardized.shape[1], 1)),
#     tf.keras.layers.MaxPooling1D(2),
#     tf.keras.layers.Flatten(),
#     tf.keras.layers.Dense(128, activation='relu'),
#     tf.keras.layers.Dropout(0.3),
#     tf.keras.layers.Dense(64, activation='relu'),
#     tf.keras.layers.Dropout(0.3),
#     tf.keras.layers.Dense(1, activation='sigmoid')
# ])

# # Použití Adam optimizer s learning rate schedulerem
# lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
#     initial_learning_rate=1e-3,
#     decay_steps=10000,
#     decay_rate=0.9
# )
# optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

# # Kompilace modelu
# model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy', tf.keras.metrics.Recall()])

# # Trénování modelu
# history = model.fit(X_train_standardized, y_train, epochs=50, verbose=0, shuffle=False)

# # Predikce
# y_pred_prob = model.predict(X_test_standardized)
# y_pred = (y_pred_prob > 0.5).astype(int)

# # Výpočet metrik
# recall = recall_score(y_test, y_pred)
# conf_matrix = confusion_matrix(y_test, y_pred)

# # Vyhodnocení výkonu modelu
# accuracy = accuracy_score(y_test, y_pred)
# precision = precision_score(y_test, y_pred)
# f1 = f1_score(y_test, y_pred)

# # Výpis metrik
# print(f"Recall: {recall:.4f}")
# print(f"Accuracy: {accuracy:.4f}")
# print(f"Precision: {precision:.4f}")
# print(f"F1 Score: {f1:.4f}")
# print(f"Confusion Matrix:\n{conf_matrix}")

# # Zobrazení confusion matrix
# sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
# plt.xlabel('Predicted')
# plt.ylabel('Actual')
# plt.title('Confusion Matrix')
# plt.show()

# import numpy as np
# import tensorflow as tf
# import matplotlib.pyplot as plt
# from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
# import seaborn as sns
# from tqdm.notebook import tqdm_notebook

# # Výpočet průměru a směrodatné odchylky pro každý sloupec
# mu = np.mean(X_train, axis=0)
# sigma = np.std(X_train, axis=0)

# # Normalizace každého sloupce zvlášť
# X_train_standardized = (X_train - mu) / sigma

# # Normalizace testovacích dat
# X_test_standardized = (X_test - mu) / sigma

# # Převedení dat na správný tvar pro CNN (přidání kanálu)
# X_train_standardized = X_train_standardized[..., np.newaxis]
# X_test_standardized = X_test_standardized[..., np.newaxis]

# # Definice modelu
# model = tf.keras.Sequential([
#     tf.keras.layers.Conv1D(32, 2, activation='relu', input_shape=(X_train_standardized.shape[1], 1)),
#     tf.keras.layers.MaxPooling1D(2),
#     tf.keras.layers.Conv1D(64, 2, activation='relu'),
#     tf.keras.layers.MaxPooling1D(2),
#     tf.keras.layers.Flatten(),
#     tf.keras.layers.Dense(128, activation='relu'),
#     tf.keras.layers.Dropout(0.3),
#     tf.keras.layers.Dense(64, activation='relu'),
#     tf.keras.layers.Dropout(0.3),
#     tf.keras.layers.Dense(1, activation='sigmoid')
# ])

# # Použití Adam optimizer s learning rate schedulerem
# lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
#     initial_learning_rate=1e-3,
#     decay_steps=10000,
#     decay_rate=0.9
# )
# optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

# # Kompilace modelu
# model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy', tf.keras.metrics.Recall()])

# # Trénování modelu
# history = model.fit(X_train_standardized, y_train, epochs=50, verbose=0, shuffle=False)

# # Predikce
# y_pred_prob = model.predict(X_test_standardized)
# y_pred = (y_pred_prob > 0.5).astype(int)

# # Výpočet metrik
# recall = recall_score(y_test, y_pred)
# conf_matrix = confusion_matrix(y_test, y_pred)

# # Vyhodnocení výkonu modelu
# accuracy = accuracy_score(y_test, y_pred)
# precision = precision_score(y_test, y_pred)
# f1 = f1_score(y_test, y_pred)

# # Výpis metrik
# print(f"Recall: {recall:.4f}")
# print(f"Accuracy: {accuracy:.4f}")
# print(f"Precision: {precision:.4f}")
# print(f"F1 Score: {f1:.4f}")
# print(f"Confusion Matrix:\n{conf_matrix}")

# # Zobrazení confusion matrix
# sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
# plt.xlabel('Predicted')
# plt.ylabel('Actual')
# plt.title('Confusion Matrix')
# plt.show()

# # Odstranění mezivýstupů
# num_iterations = 500

# best_row = None
# best_prediction = None
# best_diff = float('inf')

# for _ in range(num_iterations):
#     new_data = np.random.normal(mu, sigma)
#     new_data_standardized = (new_data - mu) / sigma
#     new_data_standardized = new_data_standardized[np.newaxis, ..., np.newaxis]  # Přidání nového rozměru pro CNN
#     prediction_prob = model.predict(new_data_standardized, verbose=0)[0][0]
#     diff = abs(prediction_prob - 0.67)

#     if diff < best_diff:
#         best_diff = diff
#         best_row = new_data
#         best_prediction = prediction_prob

# print(f"Nejlepší řádek: {best_row}")
# print(f"Predikovaná hodnota: {best_prediction}")
# print(f"Rozdíl: {best_diff}")

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
import seaborn as sns
from tqdm.notebook import tqdm_notebook

# Výpočet průměru a směrodatné odchylky pro každý sloupec
mu = np.mean(X_train, axis=0)
sigma = np.std(X_train, axis=0)

# Normalizace každého sloupce zvlášť
X_train_standardized = (X_train - mu) / sigma

# Normalizace testovacích dat
X_test_standardized = (X_test - mu) / sigma

# Převedení dat na správný tvar pro CNN (přidání kanálu)
X_train_standardized = X_train_standardized[..., np.newaxis]
X_test_standardized = X_test_standardized[..., np.newaxis]

# Definice modelu
model = tf.keras.Sequential([
    tf.keras.layers.Conv1D(32, 1, activation='relu', input_shape=(X_train_standardized.shape[1], 1)),
    tf.keras.layers.MaxPooling1D(1),
    tf.keras.layers.Conv1D(64, 1, activation='relu'),
    tf.keras.layers.MaxPooling1D(1),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Použití Adam optimizer s learning rate schedulerem
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate=1e-3,
    decay_steps=10000,
    decay_rate=0.9
)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

# Kompilace modelu
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy', tf.keras.metrics.Recall()])

# Trénování modelu
history = model.fit(X_train_standardized, y_train, epochs=50, verbose=0, shuffle=False)

# Predikce
y_pred_prob = model.predict(X_test_standardized)
y_pred = (y_pred_prob > 0.5).astype(int)

# Výpočet metrik
recall = recall_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)

# Vyhodnocení výkonu modelu
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

# Výpis metrik
print(f"Recall: {recall:.4f}")
print(f"Accuracy: {accuracy:.4f}")
print(f"Precision: {precision:.4f}")
print(f"F1 Score: {f1:.4f}")
print(f"Confusion Matrix:\n{conf_matrix}")

# Zobrazení confusion matrix
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

# Odstranění mezivýstupů
num_iterations = 500

best_row = None
best_prediction = None
best_diff = float('inf')

for _ in range(num_iterations):
    new_data = np.random.normal(mu, sigma)
    new_data_standardized = (new_data - mu) / sigma
    new_data_standardized = new_data_standardized[np.newaxis, ..., np.newaxis]  # Přidání nového rozměru pro CNN
    prediction_prob = model.predict(new_data_standardized, verbose=0)[0][0]
    diff = abs(prediction_prob - 0.67)

    if diff < best_diff:
        best_diff = diff
        best_row = new_data
        best_prediction = prediction_prob

print(f"Nejlepší řádek: {best_row}")
print(f"Predikovaná hodnota: {best_prediction}")
print(f"Rozdíl: {best_diff}")

# # Odstranění mezivýstupů
# num_iterations = 50

# best_row = None
# best_prediction = None
# best_diff = float('inf')

# for _ in range(num_iterations):
#     new_data = np.random.normal(mu, sigma)
#     new_data_standardized = (new_data - mu) / sigma
#     prediction_prob = model.predict(np.array([new_data_standardized]), verbose=0)[0][0]
#     diff = abs(prediction_prob - 0.67)

#     if diff < best_diff:
#         best_diff = diff
#         best_row = new_data
#         best_prediction = prediction_prob

# print(f"Nejlepší řádek: {best_row}")
# print(f"Predikovaná hodnota: {best_prediction}")
# print(f"Rozdíl: {best_diff}")

# Vizualizace výsledků pomocí PCA
X_standardized = (X - mu) / sigma
pca = PCA(n_components=2)  # Snížení na 2 komponenty
X_pca = pca.fit_transform(X_standardized)

plt.figure()
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y)
plt.xlabel('První hlavní komponenta')
plt.ylabel('Druhá hlavní komponenta')
plt.title('PCA na vašich datech')
plt.show()

# Vizualizace výsledků pomocí LDA
lda = LDA(n_components=1)
X_lda = lda.fit_transform(X_standardized, y)

plt.figure()
plt.scatter(X_lda[:, 0], np.zeros_like(X_lda), c=y)
plt.xlabel('První diskriminační komponenta')
plt.title('LDA s učitelem')
plt.show()

# Vytvoření obrazu pro trénovací data
min_pixel_value = -3
max_pixel_value = 3

image_training = np.zeros((len(X_train_standardized), len(X_train_standardized[0]) + 1, 3), dtype=np.uint8)

for i, label in enumerate(y_train):
    for j in range(len(X_train_standardized[0])):
        pixel_value = int(np.interp(X_train_standardized[i][j], [min_pixel_value, max_pixel_value], [0, 255]))
        image_training[i, j] = np.array([pixel_value] * 3)
    image_training[i, -1] = np.array([128, 128, 128])  # Šedý sloupec pro všechny řádky
    if label == 0:
        image_training[i, -1] = np.array([0, 128, 0])  # Zelený sloupec pro label 0
    elif label == 1:
        image_training[i, -1] = np.array([255, 0, 0])  # Červený sloupec pro label 1

# Zobrazení obrazu
#plt.imshow(image_training)
#plt.title("Training Data")#
##plt.axis("off")
#plt.show()