🔬 因子分析/降维

import numpy as np
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import scipy.stats as stats

# 数据标准化
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# PCA分析
pca = PCA(n_components=n_components)
principal_components = pca.fit_transform(X_scaled)

# 方差解释比例
explained_variance_ratio = pca.explained_variance_ratio_
cumulative_variance = np.cumsum(explained_variance_ratio)

# 因子载荷矩阵
loadings = pca.components_.T * np.sqrt(pca.explained_variance_)

# KMO和Bartlett检验
correlation_matrix = np.corrcoef(X_scaled.T)
kmo_value = calculate_kmo(correlation_matrix)
bartlett_stat, bartlett_p = stats.bartlett(*X_scaled.T)