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#!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import fetch_openml
import umap
mnist = fetch_openml("Fashion-MNIST", version=1)
trans = umap.UMAP(
n_neighbors=10,
random_state=42,
metric="euclidean",
output_metric="euclidean",
init="spectral",
verbose=True,
).fit(mnist.data)
corners = np.array(
[
[-5.1, 2.9],
[-1.9, 6.4],
[-5.4, -6.3],
[8.3, 4.0],
] # 7 # 4 # 1 # 0
)
test_pts = np.array(
[
(corners[0] * (1 - x) + corners[1] * x) * (1 - y)
+ (corners[2] * (1 - x) + corners[3] * x) * y
for y in np.linspace(0, 1, 10)
for x in np.linspace(0, 1, 10)
]
)
inv_transformed_points = trans.inverse_transform(test_pts)
plt.scatter(
trans.embedding_[:, 0],
trans.embedding_[:, 1],
cmap="Spectral",
s=0.25,
)
plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10))
plt.scatter(test_pts[:, 0], test_pts[:, 1], marker="x", c="k")
fig, ax = plt.subplots(10, 10)
for i in range(10):
for j in range(10):
ax[i, j].imshow(
inv_transformed_points[i * 10 + j].reshape(28, 28), origin="upper"
)
ax[i, j].get_xaxis().set_visible(False)
ax[i, j].get_yaxis().set_visible(False)
plt.show()
plt.close()
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