Question

[A,I] = histc([0.9828    0.4662    0.5245    0.9334    0.2163],[0.0191    0.2057    0.2820    0.2851    1.0000])

这是带有结果的MATLAB代码：

A =

     0     1     0     4     0


I =

     4     4     4     4     2

我需要的是我。我尝试过使用np.histogram，但它给了我这个：

>>> a,b = np.histogram([0.9828 ,   0.4662 ,   0.5245 ,   0.9334 ,   0.2163],[0.0191   , 0.2057   , 0.2820  ,  0.2851  ,  1.0000])
>>> a
array([0, 1, 0, 4])
>>> b
array([ 0.0191,  0.2057,  0.282 ,  0.2851,  1.    ])

我想得到数组/矩阵中每个元素都进入的bin。

Answer 1

您要找的是numpy.digitize：

返回输入数组中每个值所属的bin的索引。

>>> a = np.digitize([0.9828 ,   0.4662 ,   0.5245 ,   0.9334 ,   0.2163],[0.0191   , 0.2057   , 0.2820  ,  0.2851  ,  1.0000])
>>> print(a)
[4 4 4 4 2]

Answer 2

以下是matlab的histc的正确python实现：

import { AbstractControl } from '@angular/forms';
import { Observable, Observer, of } from 'rxjs';

export const mimeType = (
  control: AbstractControl
): Promise<{ [key: string]: any }> | Observable<{ [key: string]: any }> => {
  if (!control.value || typeof(control.value) === 'string') {  return of(null); }
  const file = control.value as File;
  const fileReader = new FileReader();
  const frObs = Observable.create(
    (observer: Observer<{ [key: string]: any }>) => {
      fileReader.addEventListener('loadend', () => {
        const arr = new Uint8Array(fileReader.result as ArrayBuffer ).subarray(0, 4);
        let header = '';
        let isValid = false;
        for (let i = 0; i < arr.length; i++) {
          header += arr[i].toString(16);
        }
        switch (header) {
          case '89504e47':
            isValid = true;
            break;
          case 'ffd8ffe0':
          case 'ffd8ffe1':
          case 'ffd8ffe2':
          case 'ffd8ffe3':
          case 'ffd8ffe8':
            isValid = true;
            break;
          case '25504446':
            isValid = true;
            break;
          default:
            isValid = false; // Or you can use the blob.type as fallback
            break;
        }
        if (isValid) {
          observer.next(null);
        } else {
          observer.next({ invalidMimeType: true });
        }
        observer.complete();
      });
      fileReader.readAsArrayBuffer(file);
    }
  );
  return frObs;
};

Answer 3

仅

numpy.digitize不是Matlab histc的完整复制品。这有效：

import numpy as np

def histc(X, bins):
    map_to_bins = np.digitize(X,bins)
    r = np.zeros(bins.shape)
    for i in map_to_bins:
        r[i-1] += 1
    return [r, map_to_bins]

if __name__=="__main__":
    X = np.array([0.9828, 0.4662, 0.5245, 0.9334, 0.2163])
    bins = np.array([0.0191, 0.2057, 0.2820, 0.2851, 1.0])
    [A,I] = histc(X, bins)
    print("X", X)
    print("bins", bins)
    print("A",A,"expecting", [0, 1, 0, 4, 0])
    print("I",I,"expecting", [4, 4, 4, 4, 2])

Answer 4

在python中正确实现MATLAB histc函数（source）。

def histc(x, bins):
    map_to_bins = np.digitize(x, bins) # Get indices of the bins to which each value in input array belongs.
    res = np.zeros(bins.shape)
    for el in map_to_bins:
        res[el-1] += 1 # Increment appropriate bin.
    return res

如何在numpy中复制这个matlab函数？

4 个答案: