import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Sample dataset (series and next numbers)
data = {
    'series': [
        [1, 2, 3],
        [2, 4, 6],
        [3, 6, 9],
        [5, 10, 15],
        [2, 4, 8],
        [1, 1, 2, 3],
        # Add more series
    ],
    'next': [4, 8, 12, 20, 16, 5]  # corresponding next numbers
}

# Preparing the dataset
X = []
y = []

for entry in data['series']:
    # Using the last 3 numbers as features
    if len(entry) >= 3:
        X.append(entry[-3:])  # last three numbers
        y.append(data['next'][data['series'].index(entry)])  # corresponding next number

X = np.array(X)
y = np.array(y)

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict the next number
def predict_next_number(series):
    if len(series) < 3:
        raise ValueError("Series must have at least 3 numbers")
    series_input = np.array(series[-3:]).reshape(1, -1)  # reshape for a single prediction
    return model.predict(series_input)[0]

# Example usage
series_to_predict = [5, 10, 15]
next_number = predict_next_number(series_to_predict)
print(f"The next number in the series {series_to_predict} is: {next_number:.2f}")