85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
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import random
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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# -------------------------------
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# Prediction Algorithm
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# -------------------------------
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def predict_next(sequence):
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"""
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Predicts the next number in a sequence of numbers using a simple linear regression.
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If there's only one number (or none), return the last value.
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Parameters:
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sequence (list of float): the list of historical numbers.
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Returns:
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float: the predicted next number.
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"""
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# Not enough data to perform a regression — just return last value.
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if len(sequence) < 2:
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return sequence[-1] if sequence else 0
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# Create an array of indices corresponding to the sequence
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x_vals = np.arange(len(sequence))
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# Compute a linear fit (degree 1 polynomial: y = m*x + b)
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m, b = np.polyfit(x_vals, sequence, 1)
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# Predict at the next index (x = len(sequence))
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return m * len(sequence) + b
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# -------------------------------
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# Data and Parameters for Plotting
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# -------------------------------
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# Lists to store the actual random numbers and the predictions.
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x_data = [] # x-axis indices
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y_data = [] # actual random numbers
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predictions = [] # predicted values
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max_points = 10000 # maximum number of data points to show on the graph
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# -------------------------------
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# Set Up the Plot
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# -------------------------------
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fig, ax = plt.subplots()
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line_actual, = ax.plot([], [], 'bo-', label="Actual Random Numbers")
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line_pred, = ax.plot([], [], 'ro--', label="Predicted Next Number")
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ax.set_xlim(0, max_points)
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ax.set_ylim(0, 110)
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ax.set_xlabel("Time (Index)")
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ax.set_ylabel("Value")
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ax.set_title("Sequence Prediction")
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ax.legend(loc='upper left')
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# -------------------------------
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# Update Function for Animation
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# -------------------------------
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def update(frame):
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# Append next index.
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next_index = len(x_data)
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x_data.append(next_index)
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new_value = random.randint(0, 100)
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y_data.append(new_value)
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pred_value = predict_next(y_data)
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predictions.append(pred_value)
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if next_index > max_points:
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ax.set_xlim(next_index - max_points, next_index)
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line_actual.set_data(x_data, y_data)
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line_pred.set_data(x_data, predictions)
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return line_actual, line_pred
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ani = animation.FuncAnimation(fig, update, interval=10, blit=True)
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# -------------------------------
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# Run the Plot
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# -------------------------------
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plt.show()
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