Hi,
I am trying to implement the LDA algorithm using the sklearn, in python
The code is:
import numpy as np
from sklearn.lda import LDA
X = np.array ([[0.000000, 0.000000, 0.000000, 0.000000, 0.001550,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.201550, 0.011111, 0.077778,
0.011111, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.092732, 0.000000, 0.000000, 0.000000,
0.000000, 0.035659, 0.000000, 0.000000, 0.000000,
0.000000, 0.066667, 0.000000, 0.000000, 0.010853,
0.000000, 0.033333, 0.055556, 0.055556, 0.077778,
0.000000, 0.000000, 0.000000, 0.268170, 0.000000,
0.000000, 0.000000, 0.000000, 0.130233, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.034109, 0.077778, 0.055556, 0.011111,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.155388, 0.000000, 0.000000, 0.000000, 0.000000,
0.181395, 0.000000, 0.000000, 0.000000, 0.000000,
0.001550, 0.007752, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.011111, 0.088889, 0.033333,
0.000000, 0.000000, 0.142857, 0.000000, 0.000000,
0.000000, 0.000000, 0.093023, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.009302, 0.010853,
0.000000, 0.100000, 0.000000, 0.000000, 0.000000,
0.000000, 0.022222, 0.088889, 0.033333, 0.238095,
0.000000, 0.000000, 0.000000, 0.000000, 0.032558,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.182946, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.022222, 0.077778, 0.055556,
0.000000, 0.102757],
[0.000000, 0.000000, 0.000000, 0.000000, 0.001550,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.201550, 0.011111, 0.077778,
0.011111, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.092732, 0.000000, 0.000000, 0.000000,
0.000000, 0.035659, 0.000000, 0.000000, 0.000000,
0.000000, 0.066667, 0.000000, 0.000000, 0.010853,
0.000000, 0.033333, 0.055556, 0.055556, 0.077778,
0.000000, 0.000000, 0.000000, 0.268170, 0.000000,
0.000000, 0.000000, 0.000000, 0.130233, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.034109, 0.077778, 0.055556, 0.011111,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.155388, 0.000000, 0.000000, 0.000000, 0.000000,
0.181395, 0.000000, 0.000000, 0.000000, 0.000000,
0.001550, 0.007752, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.011111, 0.088889, 0.033333,
0.000000, 0.000000, 0.142857, 0.000000, 0.000000,
0.000000, 0.000000, 0.093023, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.009302, 0.010853,
0.000000, 0.100000, 0.000000, 0.000000, 0.000000,
0.000000, 0.022222, 0.088889, 0.033333, 0.238095,
0.000000, 0.000000, 0.000000, 0.000000, 0.032558,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.182946, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.022222, 0.077778, 0.055556,
0.000000, 0.102757]])
y = np.array ([[0.000000, 0.000000, 0.008821, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.179631, 0.010471, 0.036649,
0.026178, 0.000000, 0.000000, 0.020942, 0.010471,
0.000000, 0.109215, 0.000000, 0.000000, 0.060144,
0.000000, 0.042502, 0.000000, 0.005613, 0.000000,
0.000000, 0.018444, 0.000000, 0.000000, 0.013633,
0.020942, 0.031414, 0.083770, 0.015707, 0.041885,
0.041885, 0.057592, 0.010471, 0.233788, 0.000000,
0.000000, 0.018444, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.090617, 0.000000, 0.000000,
0.000000, 0.104250, 0.005236, 0.020942, 0.031414,
0.000000, 0.000000, 0.010471, 0.015707, 0.005236,
0.056314, 0.000000, 0.000000, 0.026464, 0.000000,
0.004010, 0.000000, 0.031275, 0.007217, 0.036889,
0.007217, 0.013633, 0.000000, 0.000000, 0.005236,
0.047120, 0.057592, 0.015707, 0.010471, 0.047120,
0.062827, 0.005236, 0.262799, 0.000000, 0.000000,
0.000000, 0.000000, 0.000802, 0.000000, 0.000000,
0.000000, 0.001604, 0.000000, 0.052927, 0.000000,
0.039294, 0.026178, 0.041885, 0.031414, 0.000000,
0.000000, 0.041885, 0.073298, 0.000000, 0.308874,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.236568, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.020942, 0.015707,
0.000000, 0.029010,
0.000000, 0.000000, 0.008821, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.179631, 0.010471, 0.036649,
0.026178, 0.000000, 0.000000, 0.020942, 0.010471,
0.000000, 0.109215, 0.000000, 0.000000, 0.060144,
0.000000, 0.042502, 0.000000, 0.005613, 0.000000,
0.000000, 0.018444, 0.000000, 0.000000, 0.013633,
0.020942, 0.031414, 0.083770, 0.015707, 0.041885,
0.041885, 0.057592, 0.010471, 0.233788, 0.000000,
0.000000, 0.018444, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.090617, 0.000000, 0.000000,
0.000000, 0.104250, 0.005236, 0.020942, 0.031414,
0.000000, 0.000000, 0.010471, 0.015707, 0.005236,
0.056314, 0.000000, 0.000000, 0.026464, 0.000000,
0.004010, 0.000000, 0.031275, 0.007217, 0.036889,
0.007217, 0.013633, 0.000000, 0.000000, 0.005236,
0.047120, 0.057592, 0.015707, 0.010471, 0.047120,
0.062827, 0.005236, 0.262799, 0.000000, 0.000000,
0.000000, 0.000000, 0.000802, 0.000000, 0.000000,
0.000000, 0.001604, 0.000000, 0.052927, 0.000000,
0.039294, 0.026178, 0.041885, 0.031414, 0.000000,
0.000000, 0.041885, 0.073298, 0.000000, 0.308874,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.236568, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.020942, 0.015707,
0.000000, 0.029010
],
[0.000000, 0.000000, 0.008821, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.179631, 0.010471, 0.036649,
0.026178, 0.000000, 0.000000, 0.020942, 0.010471,
0.000000, 0.109215, 0.000000, 0.000000, 0.060144,
0.000000, 0.042502, 0.000000, 0.005613, 0.000000,
0.000000, 0.018444, 0.000000, 0.000000, 0.013633,
0.020942, 0.031414, 0.083770, 0.015707, 0.041885,
0.041885, 0.057592, 0.010471, 0.233788, 0.000000,
0.000000, 0.018444, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.090617, 0.000000, 0.000000,
0.000000, 0.104250, 0.005236, 0.020942, 0.031414,
0.000000, 0.000000, 0.010471, 0.015707, 0.005236,
0.056314, 0.000000, 0.000000, 0.026464, 0.000000,
0.004010, 0.000000, 0.031275, 0.007217, 0.036889,
0.007217, 0.013633, 0.000000, 0.000000, 0.005236,
0.047120, 0.057592, 0.015707, 0.010471, 0.047120,
0.062827, 0.005236, 0.262799, 0.000000, 0.000000,
0.000000, 0.000000, 0.000802, 0.000000, 0.000000,
0.000000, 0.001604, 0.000000, 0.052927, 0.000000,
0.039294, 0.026178, 0.041885, 0.031414, 0.000000,
0.000000, 0.041885, 0.073298, 0.000000, 0.308874,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.236568, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.020942, 0.015707,
0.000000, 0.029010 ]
])
clf = LDA()
clf.fit(X, y)
print(clf.predict(2, 1))
But, I got the message error:
clf.fit(X, y)
fac = 1 / (n_samples - n_classes)
ZeroDivisionError: float division by zero
What I do to solve this error?
I am using this version of the LDA, from SKLEARN
http://scikit-learn.org/stable/modules/generated/sklearn.lda.LDA.html
The second question is:
Can I use sklearn.lda with 2 .txt files? Files have 68.830 kB and 174.317
KB. The first one is a test file and the second is training file.
How I can use them, some suggestion?
Thank you very much!
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