<s> """<STR_LIT>""" <EOL> import sklearn <EOL> import numpy as np <EOL> import pylab as plt <EOL> from matplotlib import offsetbox <EOL> from sklearn import manifold , datasets , decomposition , lda <EOL> from windml . visualization . plot_timeseries import plot_timeseries <EOL> from windml . datasets . nrel import NREL <EOL> K = <NUM_LIT:30> <EOL> ds = NREL ( ) <EOL> windpark = ds . get_windpark ( NREL . park_id [ '<STR_LIT>' ] , <NUM_LIT:20> , <NUM_LIT> ) <EOL> X = np . array ( windpark . get_powermatrix ( ) ) <EOL> y = np . array ( X [ : , - <NUM_LIT:1> ] ) <EOL> X = X [ : <NUM_LIT:1000> ] <EOL> y = y [ : <NUM_LIT:1000> ] <EOL> def plot_embedding ( X , title = None , j = <NUM_LIT:1> ) : <EOL> x_min , x_max = np . min ( X , <NUM_LIT:0> ) , np . max ( X , <NUM_LIT:0> ) <EOL> X = ( X - x_min ) / ( x_max - x_min ) <EOL> ax = plt . subplot ( <NUM_LIT:2> , <NUM_LIT:2> , j ) <EOL> for i in range ( X . shape [ <NUM_LIT:0> ] ) : <EOL> plt . text ( X [ i , <NUM_LIT:0> ] , X [ i , <NUM_LIT:1> ] , str ( int ( y [ i ] ) ) , <EOL> color = plt . cm . jet ( y [ i ] / <NUM_LIT> ) , <EOL> fontdict = { '<STR_LIT>' : '<STR_LIT>' , '<STR_LIT:size>' : <NUM_LIT:9> } ) <EOL> if title is not None : <EOL> plt . title ( title ) <EOL> j = <NUM_LIT:1> <EOL> figure = plt . figure ( figsize = ( <NUM_LIT:15> , <NUM_LIT:10> ) ) <EOL> title = "<STR_LIT>" <EOL> print "<STR_LIT>" <EOL> X_pca = decomposition . RandomizedPCA ( n_components = <NUM_LIT:2> ) . fit_transform ( X ) <EOL> plot_embedding ( X_pca , <EOL> "<STR_LIT>" + title , j = <NUM_LIT:1> ) <EOL> print "<STR_LIT>" <EOL> X2 = X . copy ( ) <EOL> X2 . flat [ : : X . shape [ <NUM_LIT:1> ] + <NUM_LIT:1> ] += <NUM_LIT> <EOL> X_lda = lda . LDA ( n_components = <NUM_LIT:2> ) . fit_transform ( X2 , y ) <EOL> plot_embedding ( X_lda , <EOL> "<STR_LIT>" + title , j = <NUM_LIT:2> ) <EOL> print "<STR_LIT>" <EOL> if ( sklearn . __version__ == "<STR_LIT>" ) : <EOL> X_iso = manifold . Isomap ( K , out_dim = <NUM_LIT:2> ) . fit_transform ( X ) <EOL> else : <EOL> X_iso = manifold . Isomap ( K , n_components = <NUM_LIT:2> ) . fit_transform ( X ) <EOL> plot_embedding ( X_iso , <EOL> "<STR_LIT>" + title , j = <NUM_LIT:3> ) <EOL> print "<STR_LIT>" <EOL> if ( sklearn . __version__ == "<STR_LIT>" ) : <EOL> clf = manifold . LocallyLinearEmbedding ( K , out_dim = <NUM_LIT:2> , method = '<STR_LIT>' ) <EOL> else : <EOL> clf = manifold . LocallyLinearEmbedding ( K , n_components = <NUM_LIT:2> , method = '<STR_LIT>' ) <EOL> X_lle = clf . fit_transform ( X ) <EOL> plot_embedding ( X_lle , <EOL> "<STR_LIT>" + title , j = <NUM_LIT:4> ) <EOL> plt . show ( ) </s>
