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pythonnumpymachine-learningartificial-intelligencelstm
Ashish
Ashish

Reputation: 1

How to make a Python program to predict the next outcome of a game (of picking a color out of two), using AI and machine learning libraries

I wanted to make a program in Python using the LSTM technique that can predict the next outcome or say probability of a picking a color out of two. The program should use AI and machine learning libraries, to read the pattern of the last 40 outcomes and hence predict the next outcome.

I have made the following program in regard to it.

from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np


def predict_next_color_lstm(outcomes):
    if len(outcomes) < 40:
        return "Error: Number of outcomes provided is less than 40."

    # Convert string input to integer sequence
    seq = [0 if x == 'r' else 1 for x in outcomes]

    # Create rolling window of 40 outcomes
    X = []
    y = []
    for i in range(len(seq) - 40):
        X.append(seq[i:i + 40])
        y.append(seq[i + 40])
    X = np.array(X)
    y = np.array(y)

    # Reshape X to fit LSTM input shape
    X = np.reshape(X, (X.shape[0], X.shape[1], 1))

    # Create LSTM model
    model = Sequential()
    model.add(LSTM(50, input_shape=(40, 1)))
    model.add(Dense(1, activation='sigmoid'))

    # Compile the model
    model.compile(loss='binary_crossentropy', optimizer='adam')

    # Train the model
    model.fit(X, y, epochs=50, batch_size=32)

    # Predict the next outcome
    last_40 = seq[-40:]
    pred = model.predict(np.array([last_40]))
    return 'r' if pred < 0.5 else 'g'


def get_input():
    # Ask the user to enter a ball color sequence of length 40
    ball_seq = input("Enter the ball color sequence of length 40 (e.g. rrggrrgrrgggrgrgrrggggrgrgrrgrgggrrgggg): ")
    return ball_seq


# _main_
ball_seq = get_input()
print("Prediction : ", predict_next_color_lstm(ball_seq))

But I am getting the following error constantly when executing it:

C:\Users\Ashish\miniconda3\python.exe C:\Users\Ashish\Desktop\pyt_pract\test_prob1.py
Enter the ball color sequence of length 40 (e.g. rrggrrgrrgggrgrgrrggggrgrgrrgrgggrrgggg): rgggrrgrgrggrrgrgrgrgrggggrrrrggrrggrgrg
Traceback (most recent call last):
  File "C:\Users\Ashish\Desktop\pyt_pract\test_prob1.py", line 50, in <module>
    print("Prediction : ", predict_next_color_lstm(ball_seq))
  File "C:\Users\Ashish\Desktop\pyt_pract\test_prob1.py", line 23, in predict_next_color_lstm
    X = np.reshape(X, (X.shape[0], X.shape[1], 1))
IndexError: tuple index out of range

Upvotes: -1

Views: 2870

Answers (1)

Andrew
Andrew

Reputation: 1

The error is likely happening because the X array isn't populated correctly or the sequence is too short.

I would suggest the following:

  • Make sure the sequence is at least 40 elements long.
  • Check that the loop generates the 40-element windows properly.
  • If there are no valid sequences, handle this with an error message.

Here’s a corrected version that ensures the data is prepared properly for the model:

def predict_next_color_lstm(outcomes):
    if len(outcomes) < 40:
        return "Error: Sequence is too short."

    seq = [0 if x == 'r' else 1 for x in outcomes]
    X, y = [], []
    for i in range(len(seq) - 40):
        X.append(seq[i:i + 40])
        y.append(seq[i + 40])
    X, y = np.array(X), np.array(y)

    if X.shape[0] == 0:
        return "Error: Insufficient data."

    X = np.reshape(X, (X.shape[0], X.shape[1], 1))

    model = Sequential()
    model.add(LSTM(50, input_shape=(40, 1)))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam')
    model.fit(X, y, epochs=50, batch_size=32)

    last_40 = seq[-40:]
    pred = model.predict(np.array([last_40]))
    return 'r' if pred < 0.5 else 'g'

Upvotes: 0

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