def compareBenchmarksOverUCIDatasets():
compare_benchmarks(preprocess2.readGermanNormalized(), "German")
compare_benchmarks(preprocess2.readIonosphereNormalized(), "Ionosphere")
compare_benchmarks(preprocess2.readMagicNormalized(), "Magic")
compare_benchmarks(preprocess2.readSpambaseNormalized(), "Spambase")
compare_benchmarks(preprocess2.readWdbcNormalized(), "WDBC")
compare_benchmarks(preprocess2.readWpbcNormalized(), "WPBC")
#compare_classifiers(preprocess2.readA8ANormalized(), "A8A")
compare_benchmarks(preprocess2.readSvmguide3Normalized(), "svmguide3")
def onlineOverUCIDatasets(shuffle_var):
olsf_online(preprocess2.readGermanNormalized(), "German", shuffle_var)
olsf_online(preprocess2.readIonosphereNormalized(), "Ionosphere", shuffle_var)
olsf_online(preprocess2.readMagicNormalized(), "Magic", shuffle_var)
olsf_online(preprocess2.readSpambaseNormalized(), "Spambase", shuffle_var)
olsf_online(preprocess2.readWdbcNormalized(), "WDBC", shuffle_var)
olsf_online(preprocess2.readWpbcNormalized(), "WPBC", shuffle_var)
olsf_online(preprocess2.readA8ANormalized(), "A8A", shuffle_var)
olsf_online(preprocess2.readSvmguide3Normalized(), "svmguide3", shuffle_var)
def streamOverUCIDatasets():
olsf_stream(preprocess2.readGermanNormalized(), "German")
olsf_stream(preprocess2.readIonosphereNormalized(), "Ionosphere")
olsf_stream(preprocess2.readMagicNormalized(), "Magic")
olsf_stream(preprocess2.readSpambaseNormalized(), "Spambase")
olsf_stream(preprocess2.readWdbcNormalized(), "WDBC")
olsf_stream(preprocess2.readWpbcNormalized(), "WPBC")
olsf_stream(preprocess2.readA8ANormalized(), "A8A")
olsf_stream(preprocess2.readSvmguide3Normalized(), "svmguide3")
np.unique(self.y))
self.weight_dict = misc.numpyArrayToDict(self.classifier.coef_[0],
list(self.X[0].keys()))
def initialize(self, training_set, training_labels):
self.X = training_set
self.y = training_labels
self.init_classifier = np.zeros(len(self.X[0])).reshape(1, -1)
def repackDict(self, values, keys):
d = {}
#print(len(keys))
#print(len(values))
for i in range(0, len(keys)):
d[keys[i]] = values[i]
return d
data = preprocess2.readGermanNormalized()
#data2=preprocess2.removeRandomData(data)
X = []
y = []
for row in data:
y.append(int(row['class_label']))
del row['class_label']
X.append(row)
c = classifier()
summary, error_vector = c.fit(X, y)
misc.plotError(error_vector, "german")
def compareClassifiersOverUCIDatasets():
compare_classifiers(preprocess2.readGermanNormalized(), "German")
def streamOverUCIDatasets():
olvf_stream(preprocess2.readGermanNormalized(), "German")