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KenoAi.py

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+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from keras import layers
+from art import text2art
+
+# Generate ASCII art with the text "KenoAi"
+ascii_art = text2art("KenoAi")
+
+print("============================================================")
+print("KenoAi")
+print("Created by: Corvus Codex")
+print("Github: https://github.com/CorvusCodex/")
+print("Licence : MIT License")
+print("Support my work:")
+print("BTC: bc1q7wth254atug2p4v9j3krk9kauc0ehys2u8tgg3")
+print("ETH & BNB: 0x68B6D33Ad1A3e0aFaDA60d6ADf8594601BE492F0")
+print("Buy me a coffee: https://www.buymeacoffee.com/CorvusCodex")
+print("============================================================")
+
+# Print the generated ASCII art
+print(ascii_art)
+print("Keno prediction artificial intelligence")
+
+# Load data from file, ignoring white spaces and accepting unlimited length numbers
+data = np.genfromtxt('data.txt', delimiter=',', dtype=int)
+
+# Replace all -1 values with 0
+data[data == -1] = 0
+
+train_data = data[:int(0.8*len(data))]
+val_data = data[int(0.8*len(data)):]
+
+max_value = 80
+
+# Get the number of features from the data
+num_features = train_data.shape[1]
+
+model = keras.Sequential()
+model.add(layers.Embedding(input_dim=max_value+1, output_dim=64))
+model.add(layers.LSTM(256))
+model.add(layers.Dense(num_features, activation='softmax'))  # Set the number of units to match the number of features
+
+# Define the learning rate schedule
+lr_schedule = keras.optimizers.schedules.ExponentialDecay(
+    initial_learning_rate=1e-2,
+    decay_steps=10000,
+    decay_rate=0.9)
+
+# Use Adam optimizer with learning rate schedule
+optimizer = keras.optimizers.Adam(learning_rate=lr_schedule)
+
+model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
+
+history = model.fit(train_data, train_data, validation_data=(val_data, val_data), epochs=100)
+
+predictions = model.predict(val_data)
+
+indices = np.argsort(predictions, axis=1)[:, -num_features:]
+predicted_numbers = np.take_along_axis(val_data, indices, axis=1)
+
+print("============================================================")
+print("Predicted Numbers:")
+for numbers in predicted_numbers[:10]:
+    print(', '.join(map(str, numbers)))
+
+print("============================================================")
+print("If you won buy me a coffee: https://www.buymeacoffee.com/CorvusCodex")
+print("Support my work:")
+print("BTC: bc1q7wth254atug2p4v9j3krk9kauc0ehys2u8tgg3")
+print("ETH & BNB: 0x68B6D33Ad1A3e0aFaDA60d6ADf8594601BE492F0")
+print("Buy me a coffee: https://www.buymeacoffee.com/CorvusCodex")
+print("============================================================")
+
+# Prevent the window from closing immediately
+input('Press ENTER to exit')