使用 python 计算一维数组和 nD 数组之间的距离

Question

我是 python 的初学者，希望您能帮助我解决问题。

我有两个文件 library.csv（9 列）和 cases.csv（8 列）我用 np.loadtxt 阅读了它们。我从库中 select 列将它们放入数组 base[]，除了最后一列，我将 cases.csv 放入数组 problems[]。我会计算问题数组中每一行与基础 [] 数组的所有行之间的马哈拉诺比斯距离，并将最小距离存储在 table.

中

这是我的代码：

# Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn

from keras.models import load_model
from scipy.spatial import distance

# [1] Get the  library.csv and  cases.scv

library = np.loadtxt("library.csv", delimiter=",")
cases = np.loadtxt("cases.csv", delimiter=",")
problems = np.loadtxt("cases.csv", delimiter=",") #cases copie

# Select columns from library to use as base cases, except solutions
base = library[:, range(library.shape[1] - 1)] # Exclude last column (solution)

 # Move through all problem cases
for i in range(problems.shape[0]):
        
        # [3.1] Get inverse covariance matrix for the base cases

  covariance_matrix = np.cov(base)                                    # Covariance
  inverse_covariance_matrix = np.linalg.pinv(covariance_matrix)       # Inverse

        # [3.2] Get case row to evaluate
  case_row = problems[i, :]
  

        # Empty distances array to store mahalanobis distances obtained comparing each library cases
  distances = np.zeros(base.shape[0])

        # [3.3] For each base cases rows
  for j in range(base.shape[0]):
    # Get base case row
       base_row = base[j, :]

    # [3.4] Calculate mahalanobis distance between case row and base cases, and store it
       distances[j] = distance.mahalanobis(case_row, base_row, inverse_covariance_matrix)

# [3.5] Returns the index (row) of the minimum value in distances calculated
  min_distance_row = np.argmin(distances)

但是我得到这个错误：

Using TensorFlow backend.

Traceback (most recent call last):
File "C:\Users\HP\Desktop\MyAlgo\mainAlgo.py", line 45, in
distances[j] = distance.mahalanobis(case_row, base_row, inverse_covariance_matrix)
File "C:\Users\HP\AppData\Local\Programs\Python\Python38\lib\site-packages\scipy\spatial\distance.py", line 1083, in mahalanobis
m = np.dot(np.dot(delta, VI), delta)
File "<array_function internals>", line 5, in dot
ValueError: shapes (8,) and (384,384) not aligned: 8 (dim 0) != 384 (dim 0)

Answer 1

您的问题似乎是 base_row 和 case_row 的长度为 8，而 covariance_matrix 包含 384 个变量，这些数字应该相同。因此无法进行矩阵乘法。

我不了解你的数据和此处的统计属性，但我的猜测是你需要在计算协方差矩阵之前转置 base。在调用 np.cov(base) 中，base 中的一行应包含单个变量的所有观察值。 https://numpy.org/devdocs/reference/generated/numpy.cov.html

使用 python 计算一维数组和 nD 数组之间的距离

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