Advanced_Probability_Calculator.py

from decimal import Decimal, getcontext
from multiprocessing import Pool
import math
from statistics import mean, stdev
from sklearn.linear_model import LinearRegression
import numpy as np

# Set precision to 200 decimal places
getcontext().prec = 200

class AdvancedProbabilityCalculator:
    def __init__(self):
        pass

    # 1. Basic Probability Calculation
    def calculate_basic_probability(self, favorable_outcomes, total_outcomes):
        if total_outcomes == 0:
            raise ValueError("Total outcomes cannot be zero.")
        return Decimal(favorable_outcomes) / Decimal(total_outcomes)

    # 2. Gaussian Distribution
    def calculate_gaussian_distribution(self, x, mean, std_dev):
        coefficient = Decimal(1) / (Decimal(std_dev) * Decimal(math.sqrt(2 * math.pi)))
        exponent = Decimal(-0.5) * ((Decimal(x) - Decimal(mean)) / Decimal(std_dev)) ** 2
        return coefficient * Decimal(math.exp(exponent))

    # 3. Binomial Distribution
    def calculate_binomial_distribution(self, n, k, p):
        if not (0 <= p <= 1):
            raise ValueError("Probability p must be between 0 and 1.")
        combination = math.comb(n, k)
        return Decimal(combination) * (Decimal(p) ** k) * (Decimal(1 - p) ** (n - k))

    # 4. Conditional Probability
    def calculate_conditional_probability(self, p_a_and_b, p_b):
        if p_b == 0:
            raise ValueError("P(B) cannot be zero.")
        return Decimal(p_a_and_b) / Decimal(p_b)

    # 5. Predictive Analysis
    def calculate_predictive_analysis(self, data):
        if not data:
            raise ValueError("Data cannot be empty.")
        return mean(data)

    # 6. Risk Assessment
    def calculate_risk_assessment(self, data):
        if len(data) < 2:
            raise ValueError("At least two data points are required for risk assessment.")
        return stdev(data)

    # 7. Custom Probability Distribution
    def calculate_custom_distribution(self, probabilities):
        if not math.isclose(sum(probabilities), 1.0, rel_tol=1e-9):
            raise ValueError("The probabilities must sum to 1.")
        return probabilities

    # 8. Time Series Analysis
    def calculate_time_series_analysis(self, data, steps=1):
        if len(data) < 2:
            raise ValueError("At least two data points are required for time series analysis.")
        x = np.arange(len(data)).reshape(-1, 1)
        y = np.array(data)
        model = LinearRegression()
        model.fit(x, y)
        future_x = np.arange(len(data), len(data) + steps).reshape(-1, 1)
        predictions = model.predict(future_x)
        return predictions.tolist()

    # 9. Advanced Data Analysis
    def calculate_advanced_analysis(self, data):
        return {
            "mean": mean(data),
            "stdev": stdev(data),
            "min": min(data),
            "max": max(data),
        }

    # 10. Parallel Processing
    def calculate_parallel(self, func, data, *args):
        with Pool() as pool:
            results = pool.starmap(func, [(item, *args) for item in data])
        return results

# Usage Example
if __name__ == "__main__":
    calc = AdvancedProbabilityCalculator()

    # Example: Basic Probability
    print("Basic Probability:", calc.calculate_basic_probability(1, 6))

    # Example: Gaussian Distribution
    print("Gaussian Distribution:", calc.calculate_gaussian_distribution(0, 0, 1))

    # Example: Binomial Distribution
    print("Binomial Distribution:", calc.calculate_binomial_distribution(10, 3, 0.5))

    # Example: Conditional Probability
    print("Conditional Probability:", calc.calculate_conditional_probability(0.2, 0.5))

    # Example: Custom Distribution
    print("Custom Distribution:", calc.calculate_custom_distribution([0.1, 0.2, 0.3, 0.4]))

    # Example: Predictive Analysis
    print("Predictive Analysis:", calc.calculate_predictive_analysis([1, 2, 3, 4, 5]))

    # Example: Risk Assessment
    print("Risk Assessment:", calc.calculate_risk_assessment([10, 12, 14, 16, 18]))

    # Example: Time Series Analysis
    print("Time Series Analysis:", calc.calculate_time_series_analysis([1, 2, 3, 4, 5], steps=3))

    # Example: Advanced Analysis
    print("Advanced Analysis:", calc.calculate_advanced_analysis([10, 20, 30, 40, 50]))