【深度学习】一维数组的聚类

  
  
from sklearn.cluster import KMeans
import numpy as np
x = np.random.random(10000)
y = x.reshape(-1,1)
km = KMeans()
km.fit(y)

  
  
from jenkspy import jenks_breaks
import numpy as np
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def goodness_of_variance_fit(array, classes):
????# get the break points
????classes = jenks_breaks(array, classes)
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????# do the actual classification
????classified = np.array([classify(i, classes) for i in array])
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????# max value of zones
????maxz = max(classified)
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????# nested list of zone indices
????zone_indices = [[idx for idx, val in enumerate(classified) if zone + 1  val] for zone in range(maxz)]
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????# sum of squared deviations from array mean
????sdam = np.sum((array - array.mean())  2)
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????# sorted polygon stats
????array_sort = [np.array([array[index] for index in zone]) for zone in zone_indices]
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????# sum of squared deviations of class means
????sdcm = sum([np.sum((classified - classified.mean())  2) for classified in array_sort])
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????# goodness of variance fit
????gvf = (sdam - sdcm) / sdam
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????return gvf
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def classify(value, breaks):
????for i in range(1, len(breaks)):
????????if value < breaks[i]:
????????????return i
????return len(breaks) - 1
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if name  ‘main’:
????gvf = 0.0
????nclasses = 2
????array = np.random.random(10000)
????while gvf < .8:
????????gvf = goodness_of_variance_fit(array, nclasses)
????????print(nclasses, gvf)
????????nclasses += 1

  
  
import numpy as np
from scipy.signal import argrelextrema
import matplotlib.pyplot as plt
from sklearn.neighbors.kde import KernelDensity
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a = np.array([10, 11, 9, 23, 21, 11, 45, 20, 11, 12]).reshape(-1, 1)
kde = KernelDensity(kernel=‘gaussian’, bandwidth=3).fit(a)
s = np.linspace(0, 50)
e = kde.score_samples(s.reshape(-1, 1))
plt.plot(s, e)
plt.show()
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mi, ma = argrelextrema(e, np.less)[0], argrelextrema(e, np.greater)[0]
print(“Minima:”, s[mi])
print(“Maxima:”, s[ma])
print(a[a < mi[0]], a[(a >= mi[0]) * (a <= mi[1])], a[a >= mi[1]])
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plt.plot(s[:mi[0] + 1], e[:mi[0] + 1], ‘r’,
???????? s[mi[0]:mi[1] + 1], e[mi[0]:mi[1] + 1], ‘g’,
???????? s[mi[1]:], e[mi[1]:], ‘b’,
???????? s[ma], e[ma], ‘go’,
???????? s[mi], e[mi], ‘ro’)
plt.show()

  
  
Minima: [17.34693878 33.67346939]
Maxima: [10.20408163 21.42857143 44.89795918]
[10 11??9 11 11 12] [23 21 20] [45]