linfa-reduction 0.2.1

A collection of dimensionality reduction techniques
Documentation 
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import numpy
import matplotlib.pyplot as plt

plt.style.use("ggplot")

data = numpy.load("diffusion_map_dataset.npy")

#print("idx,x1,y1,x2,y2,x3,y3")
#for a in enumerate(zip(data[:167], data[167:334], data[334:])):
#    print("{}, {}, {}, {}, {}, {}, {}".format(a[0], a[1][0][0], a[1][0][1], a[1][1][0], a[1][1][1], a[1][2][0], a[1][2][1]))
plt.scatter(data[:167, 0], data[:167, 1])
plt.scatter(data[167:334, 0], data[167:334, 1])
plt.scatter(data[334:, 0], data[334:, 1])

import tikzplotlib

#tikzplotlib.save("dataset.tex")

data = numpy.load("diffusion_map_embedding.npy")
#for a in data:
#    print("{}, {}".format(a[0], a[1]))
plt.scatter(data[:167, 0], data[:167, 1])
plt.scatter(data[167:334, 0], data[167:334, 1])
plt.scatter(data[334:, 0], data[334:, 1])
print("idx,x1,y1,x2,y2,x3,y3")
for a in enumerate(zip(data[:167], data[167:334], data[334:])):
    print("{}, {}, {}, {}, {}, {}, {}".format(a[0], a[1][0][0], a[1][0][1], a[1][1][0], a[1][1][1], a[1][2][0], a[1][2][1]))

#tikzplotlib.save("embedding.tex")
#print(data)
#fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')
#
#ax.scatter(data[:, 0], data[:, 1], data[:, 2])
#plt.show()