Python ScatterWithHistPlot Beispiele

Beispiel #1

__author__ = 'stanley'

from src.Utility.CrossValidation import CorssValidation
from src.Utility.ScatterWithHistPlot import ScatterWithHistPlot
from sklearn.lda import LDA
from numpy import *

nbaData = genfromtxt('../../NBA2012-15/Classification/NBA12_14.csv',
                     delimiter=',')
nba15test = genfromtxt('../../NBA2012-15/Classification/NBA15.csv',
                       delimiter=',')

label = nbaData[:, 0]
features = nbaData[:, 1:]

# show 2D result
classifier = LDA(n_components=2)
f_reduced = classifier.fit(features, label).transform(features)

show = ScatterWithHistPlot()
show.plot(f_reduced, label)

#CV
validation = CorssValidation()

validation.cv(features, label, classifier, nba15test, 'ROC for LDA')

Beispiel #2

Datei: PCA.py Projekt: Sandy4321/NBA_Machine-Learning

from src.Utility.ScatterWithHistPlot import ScatterWithHistPlot

__author__ = 'stanley'

from sklearn.decomposition import PCA, KernelPCA
from numpy import genfromtxt
from sklearn import preprocessing

nbaData = genfromtxt('../../NBA2012-15/Classification/NBA12_14.csv', delimiter=',')

label = nbaData[:,0]
features = nbaData[:,1:]

pca = PCA()
pca.fit(features)

#Print out variance
print pca.explained_variance_ratio_

# plot first 2 components
pca.n_components=2
f_reduced = pca.fit_transform(features)


showGraph = ScatterWithHistPlot()
showGraph.plot(f_reduced, label)

Beispiel #3

Datei: LDA.py Projekt: Sandy4321/NBA_Machine-Learning

from sklearn.lda import LDA
from numpy import *


nbaData = genfromtxt('../../NBA2012-15/Classification/NBA12_14.csv', delimiter=',')
nba15test = genfromtxt('../../NBA2012-15/Classification/NBA15.csv', delimiter=',')

label = nbaData[:,0]
features = nbaData[:,1:]


# show 2D result
classifier = LDA(n_components=2)
f_reduced = classifier.fit(features,label).transform(features)

show = ScatterWithHistPlot()
show.plot(f_reduced, label)

#CV
validation = CorssValidation()

validation.cv(features,label,classifier,nba15test,'ROC for LDA')

Beispiel #4

Datei: PCA.py Projekt: wudadan1990/NBA_Machine-Learning

from src.Utility.ScatterWithHistPlot import ScatterWithHistPlot

__author__ = 'stanley'

from sklearn.decomposition import PCA, KernelPCA
from numpy import genfromtxt
from sklearn import preprocessing

nbaData = genfromtxt('../../NBA2012-15/Classification/NBA12_14.csv',
                     delimiter=',')

label = nbaData[:, 0]
features = nbaData[:, 1:]

pca = PCA()
pca.fit(features)

#Print out variance
print pca.explained_variance_ratio_

# plot first 2 components
pca.n_components = 2
f_reduced = pca.fit_transform(features)

showGraph = ScatterWithHistPlot()
showGraph.plot(f_reduced, label)