Gabriel, as I already said you're using a classifier for a regression problem. Please read the doc.
Maybe : http://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html this help. hint : replace LDA by sklearn.linear_model.Ridge for example. HTH Alex On Sat, Nov 9, 2013 at 7:05 PM, Gabriel Peschl <gabrielpes...@gmail.com> wrote: > 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([1, 2])) > > 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! > > > ------------------------------------------------------------------------------ > DreamFactory - Open Source REST & JSON Services for HTML5 & Native Apps > OAuth, Users, Roles, SQL, NoSQL, BLOB Storage and External API Access > Free app hosting. Or install the open source package on any LAMP server. > Sign up and see examples for AngularJS, jQuery, Sencha Touch and Native! > http://pubads.g.doubleclick.net/gampad/clk?id=63469471&iu=/4140/ostg.clktrk > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ DreamFactory - Open Source REST & JSON Services for HTML5 & Native Apps OAuth, Users, Roles, SQL, NoSQL, BLOB Storage and External API Access Free app hosting. Or install the open source package on any LAMP server. 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