Groq-Python-agent

prompt:
Write a Python function that fits a finite mixture of normals with a
specified # of components to univariate data, using the EM algorithm,
and write a main program that tests it for data simulated from a known
mixture distribution. Write a function to display the parameters in a
formatted table. Use the function to display the true and estimated
parameters. Use numpy if helpful. Use the print function to
display results, using to_string() for a pandas dataframe.
Only output Python code. Do not give commentary.
model: qwen-2.5-coder-32b
Code ran successfully after 1 attempt (generation time: 2.349 seconds, LOC=55)!
Output:
 Component True Weight True Mean True Covariance Estimated Weight Estimated Mean Estimated Covariance
 0 0.4 0 1 0.400835 0.057349 1.097366
 1 0.6 5 1 0.599165 5.049657 0.943687
Total generation time: 2.349 seconds across 1 attempt
Run command: python main.py

# Generated from prompt file: prompt_mix.txt
# Model used: qwen-2.5-coder-32b
# Time generated: 2025年03月08日 16:45:03
# Generation time: 2.349 seconds
import numpy as np
import pandas as pd
from scipy.stats import norm
from scipy.special import logsumexp
def fit_mixture_of_normals(data, k, max_iter=100, tol=1e-6):
 n = len(data)
 np.random.seed(0)
 means = np.random.choice(data, k)
 covariances = np.random.rand(k)
 weights = np.ones(k) / k
 r = np.zeros((n, k))
 
 for _ in range(max_iter):
 # Expectation step
 for i in range(k):
 r[:, i] = weights[i] * norm.pdf(data, means[i], np.sqrt(covariances[i]))
 r /= r.sum(axis=1, keepdims=True) 
 
 # Maximization step
 s = r.sum(axis=0)
 means = (r * data.reshape(-1, 1)).sum(axis=0) / s
 covariances = ((r * (data.reshape(-1, 1) - means)**2).sum(axis=0) / s).clip(min=1e-6)
 weights = s / n
 
 if (r.sum(axis=1) > 1 + tol).any() or (r.sum(axis=1) < 1 - tol).any():
 raise ValueError("Row normalization failed")
 
 return weights, means, covariances
def display_parameters(true_params, estimated_params):
 true_weights, true_means, true_covariances = true_params
 est_weights, est_means, est_covariances = estimated_params
 
 true_df = pd.DataFrame({
 'Component': range(len(true_weights)),
 'True Weight': true_weights,
 'True Mean': true_means,
 'True Covariance': true_covariances
 })
 
 est_df = pd.DataFrame({
 'Estimated Weight': est_weights,
 'Estimated Mean': est_means,
 'Estimated Covariance': est_covariances
 })
 
 result_df = pd.concat([true_df, est_df], axis=1)
 print(result_df.to_string(index=False))
def main():
 true_weights = np.array([0.4, 0.6])
 true_means = np.array([0, 5])
 true_covariances = np.array([1, 1])
 n_samples = 1000
 
 data = np.concatenate([
 np.random.normal(true_means[0], np.sqrt(true_covariances[0]), int(true_weights[0] * n_samples)),
 np.random.normal(true_means[1], np.sqrt(true_covariances[1]), int(true_weights[1] * n_samples))
 ])
 
 estimated_weights, estimated_means, estimated_covariances = fit_mixture_of_normals(data, len(true_weights))
 
 true_params = (true_weights, true_means, true_covariances)
 estimated_params = (estimated_weights, estimated_means, estimated_covariances)
 
 display_parameters(true_params, estimated_params)
if __name__ == "__main__":
 main()