python-3.x KMeans聚类-值错误:n_samples=1应>= n_cluster

mutmk8jj  于 2022-11-19  发布在  Python
关注(0)|答案(3)|浏览(473)

我正在做一个实验,三个时间序列数据集具有不同的特点为我的实验,其格式如下。

0.086206438,10
    0.086425551,12
    0.089227066,20
    0.089262508,24
    0.089744425,30
    0.090036815,40
    0.090054172,28
    0.090377569,28
    0.090514071,28
    0.090762872,28
    0.090912691,27

第一列是timestamp。出于可重复性的原因,我共享了数据here。从第2列开始,我希望读取当前行并将其与前一行的值进行比较。如果当前值大于前一行的值,我将继续比较。如果当前值小于前一行的值,我希望除以当前值(较小)乘以先前的值(较大)。相应地,代码如下:

import numpy as np
import matplotlib.pyplot as plt

protocols = {}

types = {"data1": "data1.csv", "data2": "data2.csv", "data3": "data3.csv"}

for protname, fname in types.items():
    col_time,col_window = np.loadtxt(fname,delimiter=',').T
    trailing_window = col_window[:-1] # "past" values at a given index
    leading_window  = col_window[1:]  # "current values at a given index
    decreasing_inds = np.where(leading_window < trailing_window)[0]
    quotient = leading_window[decreasing_inds]/trailing_window[decreasing_inds]
    quotient_times = col_time[decreasing_inds]

    protocols[protname] = {
        "col_time": col_time,
        "col_window": col_window,
        "quotient_times": quotient_times,
        "quotient": quotient,
    }

    plt.figure(); plt.clf()
    plt.plot(quotient_times,quotient, ".", label=protname, color="blue")
    plt.ylim(0, 1.0001)
    plt.title(protname)
    plt.xlabel("time")
    plt.ylabel("quotient")
    plt.legend()
    plt.show()

这就产生了以下三个点--每个点对应于我共享的一个dataset
第一次
从基于上面给出的代码的图中的点可以看出,data1非常一致,其值大约为1,data2将有两个商(其值将集中在0.5或0.8附近),并且data3的值集中在两个值附近(大约0.5或0.7)。这样,给定新的数据点(用quotientquotient_times表示),我想知道它属于哪个cluster,通过构建每个数据集,堆叠这两个变换后的特征quotientquotient_times。我尝试使用KMeans集群,如下所示

from sklearn.cluster import KMeans
k_means = KMeans(n_clusters=3, random_state=0)
k_means.fit(quotient)

但这是给我一个错误:ValueError: n_samples=1 should be >= n_clusters=3.如何修复此错误?
更新:样本商数据= array([ 0.7 , 0.7 , 0.4973262 , 0.7008547 , 0.71287129, 0.704 , 0.49723757, 0.49723757, 0.70676692, 0.5 , 0.5 , 0.70754717, 0.5 , 0.49723757, 0.70322581, 0.5 , 0.49723757, 0.49723757, 0.5 , 0.49723757])

wkyowqbh

wkyowqbh1#

这样,您的quotient变量现在是 * 一个 * 样本;这里我得到了一个不同的错误消息,可能是由于不同的Python/scikit-learn版本,但本质是相同的:

import numpy as np
quotient = np.array([ 0.7 , 0.7 , 0.4973262 , 0.7008547 , 0.71287129, 0.704 , 0.49723757, 0.49723757, 0.70676692, 0.5 , 0.5 , 0.70754717, 0.5 , 0.49723757, 0.70322581, 0.5 , 0.49723757, 0.49723757, 0.5 , 0.49723757])
quotient.shape
# (20,)

from sklearn.cluster import KMeans
k_means = KMeans(n_clusters=3, random_state=0)
k_means.fit(quotient)

这会产生以下错误:

ValueError: Expected 2D array, got 1D array instead:
array=[0.7        0.7        0.4973262  0.7008547  0.71287129 0.704
 0.49723757 0.49723757 0.70676692 0.5        0.5        0.70754717
 0.5        0.49723757 0.70322581 0.5        0.49723757 0.49723757
 0.5        0.49723757].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

尽管措辞不同,但与您的数据并无不同-本质上,它表示您的数据看起来像单个样本。
遵循第一个建议(即考虑quotient包含单个 * 功能 *(列))可解决此问题:

k_means.fit(quotient.reshape(-1,1))
# result
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
    n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',
    random_state=0, tol=0.0001, verbose=0)
jgovgodb

jgovgodb2#

请尝试下面的代码。一个简短的解释我所做的:
首先,我构建了数据集sample = np.vstack((quotient_times, quotient)).T,并将其标准化,这样就可以更容易地进行聚类。接下来,我使用多个超参数(eps和min_samples)应用DBScan,直到找到一个更好地分隔点的超参数。最后,我用各自的标签绘制了数据,因为您使用的是二维数据,所以很容易看到聚类的效果。

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler

types = {"data1": "data1.csv", "data2": "data2.csv", "data3": "data3.csv"}

dataset = np.empty((0, 2))

for protname, fname in types.items():
    col_time,col_window = np.loadtxt(fname,delimiter=',').T

    trailing_window = col_window[:-1] # "past" values at a given index
    leading_window  = col_window[1:]  # "current values at a given index
    decreasing_inds = np.where(leading_window < trailing_window)[0]
    quotient = leading_window[decreasing_inds]/trailing_window[decreasing_inds]
    quotient_times = col_time[decreasing_inds]

    sample = np.vstack((quotient_times, quotient)).T
    dataset = np.append(dataset, sample, axis=0)

scaler = StandardScaler()
dataset = scaler.fit_transform(dataset)

k_means = DBSCAN(eps=0.6, min_samples=1)
k_means.fit(dataset)

colors = [i for i in k_means.labels_]

plt.figure();
plt.title('Dataset 1,2,3')
plt.xlabel("time")
plt.ylabel("quotient")
plt.scatter(dataset[:, 0], dataset[:, 1], c=colors)
plt.legend()
plt.show()

xqk2d5yq

xqk2d5yq3#

您试图创建3个聚类,而您只有1个np.array,即n_samples。
1.请尝试增加数组的数目。
1.减少群集数。
1.重新调整阵列(不确定)

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