根据上述过程,请给出完整代码
时间: 2024-03-22 17:39:22 浏览: 10
以下是Python完整代码:
```python
import numpy as np
from sklearn.decomposition import PCA
# 定义标准化函数
def standardize(matrix):
"""
matrix: 待标准化矩阵
"""
mean = np.mean(matrix, axis=0)
std = np.std(matrix, axis=0)
standardized_matrix = (matrix - mean) / std
return standardized_matrix
# 定义计算主成分函数
def get_principal_component(matrix):
"""
matrix: 标准化后的矩阵
"""
pca = PCA(n_components=1)
principal_component = pca.fit_transform(matrix)
return principal_component
# 定义计算权重函数
def get_weight(matrix):
"""
matrix: 标准化后的矩阵
"""
num_of_factors = matrix.shape[1]
weight = np.zeros(num_of_factors)
for i in range(num_of_factors):
principal_component = get_principal_component(matrix[:, i].reshape(-1, 1))
weight[i] = np.abs(principal_component)
weight /= np.sum(weight)
return weight
# 定义计算融合效应函数
def get_fusion_effect(matrix_a, matrix_b):
"""
matrix_a: 矩阵A
matrix_b: 矩阵B
"""
standardized_matrix_a = standardize(matrix_a)
standardized_matrix_b = standardize(matrix_b)
# 计算各指标的权重
weight_a = get_weight(standardized_matrix_a)
weight_b = get_weight(standardized_matrix_b)
# 对矩阵A和矩阵B进行加权平均
weighted_matrix_a = standardized_matrix_a * weight_a
weighted_matrix_b = standardized_matrix_b * weight_b
fused_matrix = (weighted_matrix_a + weighted_matrix_b) / 2
# 计算融合后的指标值
weight_fused = get_weight(fused_matrix)
fused_values = np.sum(fused_matrix * weight_fused, axis=1)
# 计算矩阵A和矩阵B与融合后的指标值之间的协同效应指数
numerator_a = np.sum(weighted_matrix_a * fused_values.reshape(-1, 1))
numerator_b = np.sum(weighted_matrix_b * fused_values.reshape(-1, 1))
denominator_a = np.sqrt(np.sum(np.square(weighted_matrix_a))) * np.sqrt(np.sum(np.square(weight_fused)))
denominator_b = np.sqrt(np.sum(np.square(weighted_matrix_b))) * np.sqrt(np.sum(np.square(weight_fused)))
if denominator_a == 0 or denominator_b == 0:
fusion_effect = 0
else:
fusion_effect = (numerator_a / denominator_a + numerator_b / denominator_b) / 2
return fusion_effect
```
调用方式:
```python
matrix_a = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])
matrix_b = np.array([[9, 8, 7],[6, 5, 4],[3, 2, 1]])
fusion_effect = get_fusion_effect(matrix_a, matrix_b)
print(fusion_effect)
```
注:这里假设矩阵A和矩阵B的行数相同,即时间序列一致。如果矩阵A和矩阵B的行数不同,可以根据实际情况进行截断或插值处理。