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storyteller-ml/animation/MocapNET-kasisnu/src/python/mnet4/dataDecomposition.py
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Kasisnu 0d662c5b9d slightly tweaked mocapnet alt container
2024-01-18 19:24:33 +05:30

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#!/usr/bin/python3
"""
Author : "Ammar Qammaz"
Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt"
License : "FORTH"
"""
import sys
import os
import numpy as np
#python3 principleComponentAnalysisTester.py --config dataset/body_configuration.json --mem 1000 --highlight 4 --mode 3
#python3 principleComponentAnalysisTool.py --config dataset/body_configuration.json --all body --mem 1000 --show --type sparsepca
#python3 principleComponentAnalysisTool.py --config dataset/upperbody_configuration.json --all upperbody --mem 1000 --show --type factoranalysis
class EmptyDecomposition:
def __init__(self,):
self.decompositionType = ""
def ok(self):
return True
def fit(self,data):
pass
def transform(self,data,selectedPCADimensions=0):
return data
def save(self,filename):
return True
def load(self,filename):
return True
#------------------------------------------------------------------
#------------------------------------------------------------------
#------------------------------------------------------------------
def getSKPCA_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import PCA
engine = PCA(n_components=numberOfDimensions)
return engine
def getTruncatedSVD_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import TruncatedSVD
engine = TruncatedSVD(n_components=numberOfDimensions)
return engine
def getLatentDirichletAllocation_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import LatentDirichletAllocation
engine = LatentDirichletAllocation(n_components=numberOfDimensions)
return engine
def getFactorAnalysis_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import FactorAnalysis
engine = FactorAnalysis(n_components=numberOfDimensions)
return engine
def getDictionaryLearning_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import DictionaryLearning
engine = DictionaryLearning(n_components=numberOfDimensions)
return engine
def getFastICA_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import FastICA
engine = FastICA(n_components=numberOfDimensions)
return engine
def getNMF_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import NMF
engine = NMF(n_components=numberOfDimensions)
return engine
def getIncrementalPCA_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import IncrementalPCA
engine = IncrementalPCA(n_components=numberOfDimensions)
return engine
def getSparsePCA_Decomposer(numberOfDimensions=20):
from sklearn.decomposition import SparsePCA
engine = SparsePCA(n_components=numberOfDimensions)
return engine
def getTSNE_Decomposer(numberOfDimensions=20):
from sklearn.manifold import TSNE
engine = TSNE(n_components=numberOfDimensions,perplexity=100, learning_rate=100, verbose=2, n_iter=350)
return engine
#------------------------------------------------------------------
#------------------------------------------------------------------
#------------------------------------------------------------------
class Decomposition():
def __init__(self,
inputData:np.array=np.array([]),
savedFile:str="",
decompositionType:str="pca",
selectedPCADimensions:int=0
):
self.disabledDecomposition = False
self.numberOfSamplesFittedOn = 0
self.decompositionType = decompositionType
self.noTransformFunction = 0 #Some methods dont have a transform function
self.trackedFiles = list() #<- all needed files should be tracked here
if (selectedPCADimensions==0):
self.numberOfSamplesFittedOn = inputData.shape[0]
else:
self.numberOfSamplesFittedOn = selectedPCADimensions
import sklearn
print("Decomposition code using Sk-Learn : ",sklearn.__version__)
print("Will try to perform : ",self.decompositionType)
#self.engine = getSKPCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
from sklearn.preprocessing import StandardScaler
self.scaler = StandardScaler()
if (self.decompositionType==""):
print("Not Performing any decomposition")
self.disabledDecomposition = True
return;
elif (self.decompositionType=="skpca"):
self.engine = getSKPCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="sparsepca"):
self.engine = getSparsePCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="incrementalpca"):
self.engine = getIncrementalPCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="fastica"):
self.engine = getFastICA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="nmf"):
self.engine = getNMF_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="dictionary"):
self.engine = getDictionaryLearning_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="factoranalysis"):
self.engine = getFactorAnalysis_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="dirichlet"):
self.engine = getLatentDirichletAllocation_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="svd"):
self.engine = getTruncatedSVD_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
elif (self.decompositionType=="tsne"):
self.engine = getTSNE_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
self.noTransformFunction = 1
elif (self.decompositionType!="pca"):
print("Could not understand decomposition Type ",self.decompositionType)
sys.exit(1)
else:
print("Using SK code for decomposition Type ",self.decompositionType)
self.engine = getSKPCA_Decomposer(numberOfDimensions=self.numberOfSamplesFittedOn)
if (savedFile!=""):
print("Loading existing fitted decomposition from ",savedFile,"..!")
self.load(savedFile)
elif (inputData.size!=0):
print("Generating new fit..!")
self.fit(inputData)
else:
print("No Decomposition input given..!")
def ok(self):
return self.numberOfSamplesFittedOn!=0
def getNumberOfExpectedSamples(self):
return self.numberOfSamplesFittedOn
def fit(self,data):
import time
startAt = time.time()
#----------------------------------------------------------------------------------------
self.numberOfSamplesFittedOn = data.shape[0]
self.scaler.fit(data)
dataTransformed = self.scaler.transform(data)
outputData = self.engine.fit_transform(dataTransformed)
#----------------------------------------------------------------------------------------
endAt = time.time()
print("Time required to fit ",self.decompositionType," was ",(endAt-startAt)/60," mins")
return outputData
def transform(self,data,selectedPCADimensions=0):
if (self.disabledDecomposition):
return data
#if (selectedPCADimensions!=self.numberOfSamplesFittedOn):
# print("INCONSISTENT TRANSFORM")
if (selectedPCADimensions==0):
if (self.noTransformFunction):
dataTransformed = self.scaler.fit_transform(data)
return self.engine.fit_transform(dataTransformed)
else:
dataTransformed = self.scaler.transform(data)
return self.engine.transform(dataTransformed)
else:
if (self.noTransformFunction):
dataTransformed = self.scaler.fit_transform(data)
return self.engine.fit_transform(dataTransformed)[:,:selectedPCADimensions] # ,n_components=selectedPCADimensions
else:
dataTransformed = self.scaler.transform(data)
return self.engine.transform(dataTransformed)[:,:selectedPCADimensions] # ,n_components=selectedPCADimensions
def save(self,filename):
print("Saving Decomposition to ",filename)
OKGREEN = '\033[92m'
FAIL = '\033[91m'
ENDC = '\033[0m'
import pickle
self.trackedFiles = list()
successfulSaves = 0
#---------------------------------------------------------------
pathToEngine = "%s.%s" % (filename,self.decompositionType)
with open(pathToEngine,'wb') as engineFile:
self.trackedFiles.append(pathToEngine)
print(OKGREEN,"Saving Decomposition Engine to ",pathToEngine,ENDC)
pickle.dump(self.engine,engineFile)
successfulSaves = successfulSaves + 1
#---------------------------------------------------------------
pathToScaler = "%s.scaler.%s" % (filename,self.decompositionType)
with open(pathToScaler,'wb') as scalerFile:
self.trackedFiles.append(pathToScaler)
print(OKGREEN,"Saving Decomposition Scaler to ",pathToScaler,ENDC)
pickle.dump(self.scaler,scalerFile)
successfulSaves = successfulSaves + 1
#---------------------------------------------------------------
return (successfulSaves==2)
def load(self,filename):
print("Loading Decomposition from ",filename)
OKGREEN = '\033[92m'
FAIL = '\033[91m'
ENDC = '\033[0m'
import pickle
self.trackedFiles = list()
successfulLoads = 0
#---------------------------------------------------------------
pathToEngine = "%s.%s" % (filename,self.decompositionType)
if (os.path.isfile(pathToEngine)):
self.trackedFiles.append(pathToEngine)
unpickleEngine = open(pathToEngine, 'rb')
print(OKGREEN,"Loaded Decomposition Engine from ",pathToEngine,ENDC)
self.engine = pickle.load(unpickleEngine) #, encoding='bytes'
successfulLoads = successfulLoads + 1
else:
print(FAIL,"Failed Loading Decomposition Engine from ",pathToEngine,ENDC)
#---------------------------------------------------------------
pathToScaler = "%s.scaler.%s" % (filename,self.decompositionType)
if (os.path.isfile(pathToEngine)):
self.trackedFiles.append(pathToScaler)
unpickleScaler = open(pathToScaler, 'rb')
print(OKGREEN,"Loading Decomposition Scaler from ",pathToScaler,ENDC)
self.scaler = pickle.load(unpickleScaler) #, encoding='bytes'
successfulLoads = successfulLoads + 1
else:
print(FAIL,"Failed Loading Decomposition Scaler from ",pathToScaler,ENDC)
#---------------------------------------------------------------
return (successfulLoads==2)
def visualize(self,data,saveToFile="",onlyScreePlotNDimensions=0,label="PCA",colors=list(),colorLabel="Highlighting PC-4",viewAzimuth=45,viewElevation=45,showScree=0):
import matplotlib
import matplotlib.pyplot as plt
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 28 }
plt.rc('font', **font)
plt.rc('xtick', labelsize=15)
plt.rc('ytick', labelsize=15)
# === Plot =========================================================================
fig = plt.figure()
fig.set_size_inches(19.2, 10.8, forward=True)
ax1 = fig.add_subplot(1, 1, 1,projection='3d')
#===================================================================================
#Number of PCA components to plot on first plot (our plot is 3D so max is 4 if we dont have a color ..! )
keepNDimensions = 3
if (len(colors)==0):
keepNDimensions = 4
#Do transform of our input using the PCA dimensions as new basis
#===================================================================================
transformedData = self.transform(data,selectedPCADimensions=keepNDimensions).real
#print("TRANSFORMED DATA : ",transformedData)
print("TRANSFORMED DATA")
print("Number of Samples : ",transformedData.shape[0])
print("Number of Dimensions : ",transformedData.shape[1])
#===================================================================================
if (len(colors)==0):
if (transformedData.shape[1]<4):
print("Not enough dimensions to plot color")
keepNDimensions = 3
else:
colors = transformedData[:,3]
colorLabel = "highlighting PC-4"
else:
print("Using provided set of colorValues")
keepNDimensions = 3
ax1.view_init(viewAzimuth,viewElevation)
#===================================================================================
if (len(colors)==0):
ax1.scatter(transformedData[:,0],transformedData[:,1],transformedData[:,2],c='red')
else:
scatter = ax1.scatter(transformedData[:,0],transformedData[:,1],transformedData[:,2],c=colors,label=colors)
ax1.legend(*scatter.legend_elements(num=5),loc="upper right", title="Values", fontsize="x-small", framealpha=0.4)
#===================================================================================
# Adding title, xlabel and ylabel
ax1.set_title('%s %s %s '%(self.decompositionType,label,colorLabel)) # Title of the plot
ax1.set_xlabel('PC-1',labelpad=30) # X-Label
ax1.set_ylabel('PC-2',labelpad=30) # Y-Label
ax1.set_zlabel('PC-3',labelpad=30) # Z-Label
#ax1.tick_params(axis='x', pad=5) #fine tune numbers of plot
plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.08)
if (saveToFile!=""):
print("Saving Figure to output file %s" % saveToFile)
fig.savefig(saveToFile)
else:
plt.show()
if __name__ == '__main__':
print("principleComponentAnalysis.py is a library")
decomp = Decomposition(savedFile='dataset/combinedModel/mocapnet4/mode1/1.0/step1_upperbody_all/upperbody_all.pca',decompositionType='pca')
sample = list()
sample.append(0.470052) #0
sample.append(0.452315) #1
sample.append(1.000000) #2
sample.append(0.479688) #3
sample.append(0.239352) #4
sample.append(1.000000) #5
sample.append(0.520312) #6
sample.append(0.185185) #7
sample.append(1.000000) #8
sample.append(0.519271) #9
sample.append(0.173148) #10
sample.append(1.000000) #11
sample.append(0.000000) #12
sample.append(0.000000) #13
sample.append(0.000000) #14
sample.append(0.450000) #15
sample.append(0.238889) #16
sample.append(1.000000) #17
sample.append(0.422917) #18
sample.append(0.337037) #19
sample.append(1.000000) #20
sample.append(0.000000) #21
sample.append(0.000000) #22
sample.append(0.000000) #23
sample.append(0.509375) #24
sample.append(0.239815) #25
sample.append(1.000000) #26
sample.append(0.500000) #27
sample.append(0.362963) #28
sample.append(1.000000) #29
sample.append(0.000000) #30
sample.append(0.000000) #31
sample.append(0.000000) #32
sample.append(0.652332) #33
sample.append(0.283472) #34
sample.append(0.547378) #35
sample.append(0.213181) #36
sample.append(0.536087) #37
sample.append(0.077135) #38
sample.append(0.214366) #39
sample.append(0.509478) #40
sample.append(0.095500) #41
sample.append(0.029691) #42
sample.append(0.167750) #43
sample.append(0.522894) #44
sample.append(0.141564) #45
sample.append(0.065052) #46
sample.append(0.050908) #47
sample.append(0.124542) #48
sample.append(0.540789) #49
sample.append(0.190115) #50
sample.append(0.112984) #51
sample.append(0.101816) #52
sample.append(0.050908) #53
sample.append(0.383942) #54
sample.append(0.270394) #55
sample.append(0.307847) #56
sample.append(0.277424) #57
sample.append(0.248705) #58
sample.append(0.254739) #59
sample.append(0.270394) #60
sample.append(0.652332) #61
sample.append(0.000000) #62
sample.append(0.547378) #63
sample.append(0.536087) #64
sample.append(0.509478) #65
sample.append(0.522894) #66
sample.append(0.540789) #67
sample.append(0.270394) #68
sample.append(0.212132) #69
sample.append(0.440391) #70
sample.append(0.248585) #71
sample.append(0.182055) #72
sample.append(0.155387) #73
sample.append(0.126341) #74
sample.append(0.113851) #75
sample.append(0.174184) #76
sample.append(0.440391) #77
sample.append(0.212132) #78
sample.append(0.678899) #79
sample.append(0.152507) #80
sample.append(0.143071) #81
sample.append(0.171002) #82
sample.append(0.175392) #83
sample.append(0.193561) #84
sample.append(0.419609) #85
sample.append(0.678899) #86
sample.append(0.300000) #87
sample.append(0.216108) #88
sample.append(0.563004) #89
sample.append(0.067397) #90
sample.append(0.029691) #91
sample.append(0.059382) #92
sample.append(0.087379) #93
sample.append(0.130105) #94
sample.append(0.306329) #95
sample.append(0.563004) #96
sample.append(0.209537) #97
sample.append(0.116016) #98
sample.append(0.154849) #99
sample.append(0.587831) #100
sample.append(0.129067) #101
sample.append(0.066885) #102
sample.append(0.083048) #103
sample.append(0.069538) #104
sample.append(0.089203) #105
sample.append(0.321928) #106
sample.append(0.587831) #107
sample.append(0.192741) #108
sample.append(0.107269) #109
sample.append(0.061752) #110
sample.append(0.094237) #111
sample.append(0.617853) #112
sample.append(0.190791) #113
sample.append(0.125269) #114
sample.append(0.133770) #115
sample.append(0.098298) #116
sample.append(0.081326) #117
sample.append(0.347944) #118
sample.append(0.617853) #119
sample.append(0.194984) #120
sample.append(0.131074) #121
sample.append(0.123504) #122
sample.append(0.061752) #123
sample.append(0.348961) #124
sample.append(0.308926) #125
sample.append(0.269400) #126
sample.append(0.236866) #127
sample.append(0.208076) #128
sample.append(0.214721) #129
sample.append(0.232589) #130
sample.append(0.040663) #131
sample.append(0.308926) #132
sample.append(0.143338) #133
sample.append(0.378955) #134
sample.append(0.265854) #135
sample.append(0.281502) #136
sample.append(0.308926) #137
sample.append(0.652332) #138
sample.append(0.000000) #139
sample.append(0.547378) #140
sample.append(0.536087) #141
sample.append(0.509478) #142
sample.append(0.522894) #143
sample.append(0.540789) #144
sample.append(0.270394) #145
sample.append(0.000000) #146
sample.append(0.440391) #147
sample.append(0.678899) #148
sample.append(0.563004) #149
sample.append(0.587831) #150
sample.append(0.617853) #151
sample.append(0.308926) #152
sample.append(0.106590) #153
sample.append(0.587454) #154
sample.append(0.178302) #155
sample.append(0.106590) #156
sample.append(0.109798) #157
sample.append(0.069458) #158
sample.append(0.052693) #159
sample.append(0.317531) #160
sample.append(0.587454) #161
sample.append(0.166465) #162
sample.append(0.146195) #163
sample.append(0.111492) #164
sample.append(0.053520) #165
sample.append(0.030413) #166
sample.append(0.278528) #167
sample.append(0.587454) #168
sample.append(3.096379) #169
sample.append(0.651328) #170
sample.append(5.955375) #171
sample.append(6.084674) #172
sample.append(-0.059619) #173
sample.append(0.048326) #174
sample.append(0.234845) #175
sample.append(0.585668) #176
sample.append(0.651328) #177
sample.append(0.686142) #178
sample.append(5.398531) #179
sample.append(5.946909) #180
sample.append(5.904308) #181
sample.append(5.806487) #182
sample.append(0.529019) #183
sample.append(0.651328) #184
sample.append(6.084674) #185
sample.append(3.792921) #186
sample.append(3.694669) #187
sample.append(4.237240) #188
sample.append(4.096264) #189
sample.append(4.071058) #190
sample.append(3.975877) #191
sample.append(3.886222) #192
sample.append(3.886222) #193
sample.append(0.000000) #194
sample.append(3.776154) #195
sample.append(4.110508) #196
sample.append(4.119073) #197
sample.append(4.020748) #198
sample.append(3.931176) #199
sample.append(3.931177) #200
sample.append(0.000000) #201
sample.append(3.929643) #202
sample.append(2.813782) #203
sample.append(1.095648) #204
sample.append(3.325809) #205
sample.append(2.449419) #206
sample.append(2.176756) #207
sample.append(2.363436) #208
sample.append(2.456802) #209
sample.append(1.402459) #210
sample.append(1.095648) #211
sample.append(2.003560) #212
sample.append(3.639651) #213
sample.append(2.840933) #214
sample.append(2.875062) #215
sample.append(2.887117) #216
sample.append(1.448436) #217
sample.append(1.095648) #218
sample.append(2.736625) #219
sample.append(2.943081) #220
sample.append(0.954672) #221
sample.append(5.591012) #222
sample.append(3.255518) #223
sample.append(1.401905) #224
sample.append(2.261431) #225
sample.append(2.461843) #226
sample.append(1.159315) #227
sample.append(0.954672) #228
sample.append(1.819818) #229
sample.append(4.163903) #230
sample.append(4.543498) #231
sample.append(3.371371) #232
sample.append(3.151165) #233
sample.append(1.170683) #234
sample.append(0.954672) #235
sample.append(2.943081) #236
sample.append(3.081973) #237
sample.append(0.929466) #238
sample.append(5.318349) #239
sample.append(4.543498) #240
sample.append(3.256703) #241
sample.append(2.719052) #242
sample.append(2.719052) #243
sample.append(1.130633) #244
sample.append(0.929466) #245
sample.append(1.897446) #246
sample.append(4.228285) #247
sample.append(4.543498) #248
sample.append(3.707124) #249
sample.append(3.371372) #250
sample.append(1.137976) #251
sample.append(0.929466) #252
sample.append(3.216783) #253
sample.append(3.189919) #254
sample.append(0.834284) #255
sample.append(5.505028) #256
sample.append(5.403023) #257
sample.append(5.860644) #258
sample.append(3.256703) #259
sample.append(2.719052) #260
sample.append(0.929466) #261
sample.append(0.834284) #262
sample.append(1.597937) #263
sample.append(4.522202) #264
sample.append(5.142685) #265
sample.append(4.364796) #266
sample.append(3.691174) #267
sample.append(0.898620) #268
sample.append(0.834284) #269
sample.append(3.574276) #270
sample.append(3.376438) #271
sample.append(0.744629) #272
sample.append(5.598395) #273
sample.append(5.603435) #274
sample.append(5.860644) #275
sample.append(5.860644) #276
sample.append(3.166879) #277
sample.append(0.744629) #278
sample.append(0.744629) #279
sample.append(1.183479) #280
sample.append(4.781023) #281
sample.append(5.403024) #282
sample.append(4.970201) #283
sample.append(4.234641) #284
sample.append(0.684905) #285
sample.append(0.744629) #286
sample.append(4.391370) #287
sample.append(3.727261) #288
sample.append(0.744629) #289
sample.append(4.544052) #290
sample.append(4.300908) #291
sample.append(4.272226) #292
sample.append(4.071058) #293
sample.append(3.886222) #294
sample.append(3.166879) #295
sample.append(0.744629) #296
sample.append(3.604797) #297
sample.append(4.254323) #298
sample.append(4.324193) #299
sample.append(4.133639) #300
sample.append(3.966107) #301
sample.append(4.234641) #302
sample.append(0.744629) #303
sample.append(3.966615) #304
sample.append(3.792921) #305
sample.append(0.000000) #306
sample.append(4.237240) #307
sample.append(4.096264) #308
sample.append(4.071058) #309
sample.append(3.975877) #310
sample.append(3.886222) #311
sample.append(3.886222) #312
sample.append(3.694669) #313
sample.append(3.776154) #314
sample.append(4.110508) #315
sample.append(4.119073) #316
sample.append(4.020748) #317
sample.append(3.931176) #318
sample.append(3.931177) #319
sample.append(0.000000) #320
sample.append(3.929643) #321
sample.append(3.827735) #322
sample.append(0.634561) #323
sample.append(5.145152) #324
sample.append(4.961411) #325
sample.append(5.039039) #326
sample.append(4.739530) #327
sample.append(4.325072) #328
sample.append(0.463204) #329
sample.append(0.634561) #330
sample.append(0.000000) #331
sample.append(4.613133) #332
sample.append(4.903871) #333
sample.append(4.607087) #334
sample.append(4.287396) #335
sample.append(0.295320) #336
sample.append(0.634561) #337
sample.append(4.346044) #338
sample.append(2.256939) #339
sample.append(0.968915) #340
sample.append(0.498058) #341
sample.append(1.022310) #342
sample.append(1.086692) #343
sample.append(1.380610) #344
sample.append(1.639430) #345
sample.append(1.112731) #346
sample.append(0.968915) #347
sample.append(1.471541) #348
sample.append(0.000000) #349
sample.append(0.927337) #350
sample.append(1.482404) #351
sample.append(1.967686) #352
sample.append(1.114843) #353
sample.append(0.968915) #354
sample.append(1.776775) #355
sample.append(2.805316) #356
sample.append(0.977481) #357
sample.append(5.982525) #358
sample.append(1.401905) #359
sample.append(1.401905) #360
sample.append(2.001093) #361
sample.append(2.261431) #362
sample.append(1.182600) #363
sample.append(0.977481) #364
sample.append(1.762278) #365
sample.append(4.068930) #366
sample.append(3.258445) #367
sample.append(2.912313) #368
sample.append(2.912314) #369
sample.append(1.196280) #370
sample.append(0.977481) #371
sample.append(2.673644) #372
sample.append(2.762715) #373
sample.append(0.879156) #374
sample.append(6.016655) #375
sample.append(0.229778) #376
sample.append(0.565532) #377
sample.append(1.223204) #378
sample.append(1.828609) #379
sample.append(0.992046) #380
sample.append(0.879156) #381
sample.append(1.465495) #382
sample.append(4.623997) #383
sample.append(6.053906) #384
sample.append(3.258445) #385
sample.append(2.912314) #386
sample.append(0.977481) #387
sample.append(0.879156) #388
sample.append(2.397369) #389
sample.append(2.664894) #390
sample.append(0.789584) #391
sample.append(6.028709) #392
sample.append(0.009572) #393
sample.append(0.229779) #394
sample.append(0.549582) #395
sample.append(1.093048) #396
sample.append(0.824514) #397
sample.append(0.789584) #398
sample.append(1.145804) #399
sample.append(5.109279) #400
sample.append(6.053906) #401
sample.append(6.053907) #402
sample.append(3.136574) #403
sample.append(0.789584) #404
sample.append(0.789584) #405
sample.append(0.819205) #406
sample.append(3.670611) #407
sample.append(0.789584) #408
sample.append(4.590029) #409
sample.append(4.312276) #410
sample.append(4.279569) #411
sample.append(4.040213) #412
sample.append(3.826498) #413
sample.append(1.093048) #414
sample.append(0.789584) #415
sample.append(3.436912) #416
sample.append(4.256435) #417
sample.append(4.337873) #418
sample.append(4.119073) #419
sample.append(3.931176) #420
sample.append(3.136574) #421
sample.append(0.789584) #422
sample.append(3.927943) #423
sample.append(3.792921) #424
sample.append(0.000000) #425
sample.append(4.237240) #426
sample.append(4.096264) #427
sample.append(4.071058) #428
sample.append(3.975877) #429
sample.append(3.886222) #430
sample.append(3.886222) #431
sample.append(0.000000) #432
sample.append(3.776154) #433
sample.append(4.110508) #434
sample.append(4.119073) #435
sample.append(4.020748) #436
sample.append(3.931176) #437
sample.append(3.931177) #438
sample.append(3.694669) #439
sample.append(3.929643) #440
sample.append(2.943082) #441
sample.append(0.788050) #442
sample.append(5.878218) #443
sample.append(6.084674) #444
sample.append(0.075190) #445
sample.append(0.432683) #446
sample.append(1.249777) #447
sample.append(0.825022) #448
sample.append(0.788050) #449
sample.append(1.204451) #450
sample.append(4.918368) #451
sample.append(5.815236) #452
sample.append(5.538962) #453
sample.append(3.960798) #454
sample.append(0.786350) #455
sample.append(0.788050) #456
sample.append(3.255518) #457
out = decomp.transform(np.asarray(sample),selectedPCADimensions=210)
for i in range(0,len(out)):
print(i," = ",out[i])
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