计算质心和精度

Question

我有两个点 feat_left, feat_right 从暹罗网络获得，我将这些点绘制在 x,y 坐标中，如下所示。

这是python脚本

import json
import matplotlib.pyplot as plt
import numpy as np



data = json.load(open('predictions-mnist.txt'))

n=len(data['outputs'].items())
label_list = np.array(range(n))
feat_left = np.random.random((n,2))


count=1

for key,val in data['outputs'].items():
    feat = data['outputs'][key]['feat_left']
    feat_left[count-1] = feat
    key = key.split("/")
    key = int(key[6])
    label_list[count - 1] = key
    count = count + 1


f = plt.figure(figsize=(16,9))

c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',
     '#ff00ff', '#990000', '#999900', '#009900', '#009999']

for i in range(10):
    plt.plot(feat_left[label_list==i,0].flatten(), feat_left[label_list==i,1].flatten(), '.', c=c[i])
plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
plt.grid()
plt.show()

现在我想计算每个簇的 centriod 然后 purity。

Answer 1

质心就是 mean:

centorids = np.zeros((10,2), dtype='f4')
for i in xrange(10):
    centroids[i,:] = np.mean( feat_left[label_list==i, :2], axis=0 )

至于准确度，您可以计算距质心的均方误差（距离）：

sqerr = np.zeros((10,), dtype='f4')
for i in xrange(10):
    sqerr[i] = np.sum( (feat_left[label_list==i, :2]-centroids[i,:])**2 )

计算 purity:

def compute_cluster_purity(gt_labels, pred_labels):
  """
  Compute purity of predicted labels (pred_labels), given 
  the ground-truth labels (gt_labels).

  Assuming gt_labels and pred_labels are both lists of int of length n
  """
  n = len(gt_labels) # number of elements
  assert len(pred_labels) == n
  purity = 0
  for l in set(pred_labels):
    # for predicted label l, what are the gt_labels of this cluster?
    gt = [gt_labels[i] for i, il in enumerate(pred_labels) if il==l]
    # most frequent gt label in this cluster:
    mfgt = max(set(gt), key=gt.count)
    purity += gt.count(mfgt) # count intersection between most frequent ground truth and this cluster
  return float(purity)/n

有关在群集中选择最频繁标签的更多详细信息，请参阅 this answer。

计算质心和精度

Compute centroid and accuracy

python

k-means

deep-learning