"""
==========================
tSNE to visualize digits
==========================

Here we use :class:`sklearn.manifold.TSNE` to visualize the digits
datasets. Indeed, the digits are vectors in a 8*8 = 64 dimensional space.
We want to project them in 2D for visualization. tSNE is often a good
solution, as it groups and separates data points based on their local
relationship.

"""

############################################################
# Load the iris data
from sklearn import datasets

digits = datasets.load_digits()
# Take the first 500 data points: it's hard to see 1500 points
X = digits.data[:500]
y = digits.target[:500]

############################################################
# Fit and transform with a TSNE
from sklearn.manifold import TSNE

tsne = TSNE(n_components=2, random_state=0)

############################################################
# Project the data in 2D
X_2d = tsne.fit_transform(X)

############################################################
# Visualize the data
target_ids = range(len(digits.target_names))

import matplotlib.pyplot as plt

plt.figure(figsize=(6, 5))
colors = "r", "g", "b", "c", "m", "y", "k", "w", "orange", "purple"
for i, c, label in zip(target_ids, colors, digits.target_names, strict=True):
    plt.scatter(X_2d[y == i, 0], X_2d[y == i, 1], c=c, label=label)
plt.legend()
plt.show()