pytutorial/scikit-learn/kmeans/README.md

# KMeans
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## The goal


Questions to [David Rotermund](mailto:davrot@uni-bremen.de)

## Test data

```python
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()
```
![image0](image0.png)

## [sklearn.cluster.KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) and its [fit](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.fit)

```python
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: 
```python
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.

```python
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()
```

![image1](image1.png)