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HorseRacing.py
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HorseRacing.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
ascii_art = text2art("HorseRacingAi")
print("============================================================")
print("HorseRacingAi")
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("Horse Racing prediction artificial intelligence")
print("============================================================")
# Load data from file, ignoring white spaces and accepting unlimited length numbers
data = np.genfromtxt('data.txt', delimiter=',', dtype=int)
# Filter rows with less than 8 or more than 8 numbers
data = data[(np.count_nonzero(data, axis=1) >= 8) & (np.count_nonzero(data, axis=1) <= 8)]
# 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 = 8
# 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(512))
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=20)
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 Winner:")
for numbers in predicted_numbers[:10]:
print(numbers[0])
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')