pytutorial/scikit-learn/kmeans/README.md

KMeans

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* TOC {:toc}

The goal

Questions to David Rotermund

Test data

import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(1)

rng = np.random.default_rng()

a_x = rng.normal(1.5, 1.0, size=(1000))
a_y = rng.normal(3.0, 1.0, size=(1000))

b_x = rng.normal(0.0, 1.0, size=(1000))
b_y = rng.normal(0.0, 1.0, size=(1000))

plt.plot(a_x, a_y, "c.")
plt.plot(b_x, b_y, "m.")
plt.show()

sklearn.cluster.KMeans and its fit

class sklearn.cluster.KMeans(n_clusters=8, *, init='k-means++', n_init='warn', max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='lloyd')

K-Means clustering.

Attribute:

cluster_centers_ : ndarray of shape (n_clusters, n_features) Coordinates of cluster centers. If the algorithm stops before fully converging (see tol and max_iter), these will not be consistent with labels_.

Method:

fit(X, y=None, sample_weight=None)

Compute k-means clustering X: {array-like, sparse matrix} of shape (n_samples, n_features) Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

rng = np.random.default_rng(1)

a_x = rng.normal(1.5, 1.0, size=(1000))[:, np.newaxis]
a_y = rng.normal(3.0, 1.0, size=(1000))[:, np.newaxis]
data_a = np.concatenate((a_x, a_y), axis=1)

b_x = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
b_y = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
data_b = np.concatenate((b_x, b_y), axis=1)

data = np.concatenate((data_a, data_b), axis=0)

kmeans = KMeans(n_clusters=2)
kmeans.fit(data)


plt.plot(a_x, a_y, "c.")
plt.plot(b_x, b_y, "m.")
plt.plot(
    kmeans.cluster_centers_[0, 0], kmeans.cluster_centers_[0, 1], "k*", markersize=12
)
plt.plot(
    kmeans.cluster_centers_[1, 0], kmeans.cluster_centers_[1, 1], "k*", markersize=12
)

plt.show()