def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
# X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
minmax_scale(X, copy=False)
res = compactness(X, y, types=types, metric=1)
print(res[0], res[1], res[2])
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
X, types, y = ToFormNumpy(
r"D:\Nuu\AI\Selections\Amazon_initial_50_30_10000\data.txt")
metric = 1
minmax_scale(X, copy=False)
w = Lagranj_nd(X, y)
value = w.min()
X_Test = np.array(X[:, w == value])
types_Test = np.array(types[w == value])
i = 0
while X_Test.shape[1] < 2000:
value = np.min(w[w > value])
X_Test = X[:, w <= value]
types_Test = types[w <= value]
noisy = find_noisy(X_Test, y, types=types_Test, metric=metric)
cond = np.logical_not(noisy)
print("\nnoisy = ", len(noisy[noisy == True]))
compactness(X_Test[cond], y[cond], types=types_Test, metric=metric)
i += 1
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
X, types, y = ToFormNumpy("D:\\tanlanmalar\\Asian Religion.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\arcene_train.txt")
minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y, types=types)
k = 10
k_fold = KFold(n_splits=k, shuffle=True, random_state=None)
# Neighbors
nnc = NearestNeighborClassifier()
knc = TemplateClassifier()
begin = time.time()
max_mean1 = CVS(nnc, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
end = time.time()
print("Time: ", (end - begin) * 1000)
print(max_mean1)
begin = time.time()
max_mean2 = CVS(knc, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
end = time.time()
print("Time: ", (end - begin) * 1000)
print(max_mean1, max_mean2)
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
X, types, y = ToFormNumpy(r"D:\Nuu\AI\Selections\Amazon_initial_50_30_10000\data.txt")
metric = 1
minmax_scale(X, copy=False)
#w = Lagranj_nd(X, y)
w = Lagranj(X)
value = w.max()
value = w.max()
cond = w == value
while len(cond[cond == True]) < 661:
value = np.max(w[w < value])
cond = w >= value
X_Test = X[:, w >= value]
k = 10
k_fold = KFold(n_splits=k, shuffle=True, random_state=None)
svm = SVC(kernel="linear")
#svm.fit(X_Test, y)
nn = MLPClassifier()
nn.fit(X_Test, y)
max_mean = CVS(nn, X_Test, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
print(max_mean)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
reg = KnnOptimalRegression()
reg.fit(X, y)
print(reg.predict(X))
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy(r"D:\Nuu\AI\Selections\Amazon_initial_50_30_10000\data.txt")
X, types, y = ToFormNumpy(
r"D:\Nuu\AI\Selections\LSVT_voice_rehabilitation\data.txt")
minmax_scale(X, copy=False)
metric = 1
w = Lagranj1(X, y)
minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y)
while X.shape[1] > 103:
cond = w != w.min()
X = X[:, cond]
w = w[cond]
nnc1 = NearestNeighborClassifier_(noisy=True)
nnc2 = NearestNeighborClassifier()
nnc3 = TemplateClassifier(noisy=True)
nn = MLPClassifier()
svm = SVC()
k = 10
mean1 = 0
mean2 = 0
mean3 = 0
mean4 = 0
mean5 = 0
for i in range(k):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.45,
random_state=None,
shuffle=True)
nnc1.fit(X_train, y_train)
nnc2.fit(X_train, y_train)
nnc3.fit(X_train, y_train)
svm.fit(X_train, y_train)
nn.fit(X_train, y_train)
mean1 += nnc1.score(X_test, y_test)
mean2 += nnc2.score(X_test, y_test)
mean3 += nnc3.score(X_test, y_test)
mean4 += svm.score(X_test, y_test)
mean5 += nn.score(X_test, y_test)
mean1 /= k
mean2 /= k
mean3 /= k
mean4 /= k
mean5 /= k
print(mean1, mean2, mean3, mean4, mean5)
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
X, types, y = ToFormNumpy(r"D:\Nuu\AI\Selections\Amazon_initial_50_30_10000\data.txt")
#y[y == 2] = 1
minmax_scale(X, copy=False)
#minmax_scale(X, copy=False)
w = Lagranj_nd(X, y)
#return 0
value = w.max()
cond = w == value
while len(cond[cond == True]) < 5000:
value = np.max(w[w < value])
cond = w >= value
if len(cond[cond == True]) > 154:
compactness(X[:, cond], y, types)
return 0
print(len(cond[cond == True]))
X = X[:, cond]
types = types[cond]
metric = 1
#nnc = NearestNeighborClassifier_(noisy=True)
nnc = NearestNeighborClassifier()
# nnc = TemplateClassifier(noisy=True)
nn = MLPClassifier()
svm = SVC()
k = 10
mean1 = 0
mean2 = 0
mean3 = 0
for i in range(k):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.5, random_state=None, shuffle=True)
nnc.fit(X_train, y_train)
svm.fit(X_train, y_train)
nn.fit(X_train, y_train)
mean1 += nnc.score(X_test, y_test)
mean2 += svm.score(X_test, y_test)
mean3 += nn.score(X_test, y_test)
mean1 /= k
mean2 /= k
mean3 /= k
print(mean1, mean2, mean3)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
y -= 1
minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y)
compactness(X, y, types=types, metric=1)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
y -= 1
file = open("test.txt", "w")
print(Fris(X, y, types=types, file=file))
file.close()
def main():
path = r"D:\tanlanmalar\GIPER_MY.txt"
X, types, y = ToFormNumpy(path)
y -= 1
minmax_scale(X, copy=False)
# Normalizing_Estmation(X, y)
print(compactness(X, y, types))
res = find_standard(X, y, types)
res = find_noisy(X, y, types)
s = 0
for i in range(res.shape[0]):
if res[i] == True and y[i] == 1:
print(i + 1)
s += 1
print(s)
return 0
#nnc = NearestNeighborClassifier_(noisy=True)
#nnc = NearestNeighborClassifier()
#nnc = TemplateClassifier(noisy=True)
nn = MLPClassifier()
svm = SVC()
k = 10
mean1 = 0
mean2 = 0
mean3 = 0
for i in range(k):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=None,
shuffle=True)
nnc.fit(X_train, y_train)
svm.fit(X_train, y_train)
nn.fit(X_train, y_train)
mean1 += nnc.score(X_test, y_test)
mean2 += svm.score(X_test, y_test)
mean3 += nn.score(X_test, y_test)
mean1 /= k
mean2 /= k
mean3 /= k
print(mean1, mean2, mean3)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
y -= 1
minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y)
group, comp1 = Fris(X, y, types=types, file=None)
def main():
#path = r"D:\Nuu\AI\Selections\leukemia\leukemia_small.csv"
#X, types, y = ReadFromCSVWithHeaderClass(path)
X, types, y = ToFormNumpy(r"D:\tanlanmalar\spame.txt")
minmax_scale(X, copy=False)
#res = find_shell(X, y, types)
#res = find_standard(X, y, types)
#res = find_noisy(X, y, types)
#count(res, y)
#X = X[res == False]
#y = y[res == False]
#print(X.shape)
#print(compactness(X, y, types))
#return 0
nnc = NearestNeighborClassifier_(noisy=False)
#nnc = NearestNeighborClassifier()
#nnc = TemplateClassifier(noisy=True)
nn = MLPClassifier()
svm = SVC()
k = 10
mean1 = 0
mean2 = 0
mean3 = 0
for i in range(k):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=None,
shuffle=True)
nnc.fit(X_train, y_train)
#svm.fit(X_train, y_train)
#nn.fit(X_train, y_train)
mean1 += nnc.score(X_test, y_test)
#mean2 += svm.score(X_test, y_test)
#mean3 += nn.score(X_test, y_test)
mean1 /= k
mean2 /= k
mean3 /= k
print(mean1, mean2, mean3)
def main():
path = r"D:\Tanlanmalar\MATBIO_MY.txt"
X, types, y = ToFormNumpy(path)
y -= 1
print(X.shape)
#minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y)
count(find_shell(X, y), y)
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
#y -= 1
#y[y == 2] = 1
for i in range(X.shape[1]):
for j in range(i + 1, X.shape[1]):
ReductionOptimal(X[:, i], X[:, j], y)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasteralogy.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
y = y - 1
#minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y)
drawobjects(X[:, [3, 5]], classes=y, isVisibleLabel=True)
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
#X, types, y = ToFormNumpy("D:\\german.txt") #71.2
#X, types, y = ToFormNumpy("D:\\german1.txt") #91.7
#X, types, y = ToFormNumpy("D:\\german2.txt") #91.7
#X, types, y = ToFormNumpy("D:\\german3.txt") #94.4
#X, types, y = ToFormNumpy("D:\\german4.txt") #95.4
#X, types, y = ToFormNumpy("D:\\german5.txt") #97.7
X, types, y = ToFormNumpy("D:\\german6.txt") #98.1
#X, types, y = ToFormNumpy("D:\\german7.txt") #97.3
#X, types, y = ToFormNumpy("D:\\german8.txt") #94.5
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\german.txt")
#y[y == 2] = 1
_, ln = np.unique(y, return_counts=True)
#minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y)
# Cross Validation
k = 10
k_fold = KFold(n_splits=k, shuffle=True, random_state=None)
#Nerual network
mlp = MLPClassifier(hidden_layer_sizes=(100, 200))
# Knn
n_neighbors = 2 * min(ln) - 3
# mertic Euclidean
#knc = KNeighborsClassifier(n_neighbors=n_neighbors)
knc = KNeighborsClassifier(n_neighbors=1)
#SVM
svc = SVC()
#print("MLP")
max_mean1 = CVS(mlp, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
#print("KNN")
max_mean2 = CVS(knc, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
#print("SVM")
max_mean3 = CVS(svc, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
print(max_mean1, max_mean2, max_mean3)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
# X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
metric = 1
minmax_scale(X, copy=False)
noisy = find_noisy(X, y, types=types, metric=metric)
#for item in noisy:
# print(item)
print(len(noisy))
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
#X, types, y = ToFormNumpy(r"D:\Nuu\Data mining\Articles\PCA operator\Computing\Gastown1.txt")
X, types, y = ToFormNumpy(r"D:\Nuu\AI\Selections\Amazon_initial_50_30_10000\data.txt")
#minmax_scale(X, copy=False)
w = Lagranj_nd(X, y)
cond = w > 0
print(len(cond[cond == True]))
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
X, types, y = ToFormNumpy("D:\\tanlanmalar\\arcene_train.txt")
minmax_scale(X, copy=False)
w = Lagranj_nd(X, y)
print(w.shape)
X_Test = np.array(X[:, w == w.min()])
types_Test = np.array(types[w == w.min()])
print(X_Test)
res = compactness(X_Test, y, types=types_Test, metric=1)
def main():
path = r"D:\Tanlanmalar\german.txt"
X, types, y = ToFormNumpy(path)
minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y)
nnc1 = NearestNeighborClassifier_(noisy=True)
nnc2 = NearestNeighborClassifier()
nnc3 = TemplateClassifier(noisy=True)
nn = MLPClassifier()
svm = SVC()
k = 10
mean1 = 0
mean2 = 0
mean3 = 0
mean4 = 0
mean5 = 0
for i in range(k):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.45, random_state=None, shuffle=True)
nnc1.fit(X_train, y_train)
nnc2.fit(X_train, y_train)
nnc3.fit(X_train, y_train)
svm.fit(X_train, y_train)
nn.fit(X_train, y_train)
mean1 += nnc1.score(X_test, y_test)
mean2 += nnc2.score(X_test, y_test)
mean3 += nnc3.score(X_test, y_test)
mean4 += svm.score(X_test, y_test)
mean5 += nn.score(X_test, y_test)
mean1 /= k
mean2 /= k
mean3 /= k
mean4 /= k
mean5 /= k
print(mean1, mean2, mean3, mean4, mean5)
def main():
path = r"D:\Nuu\AI\Selections\Amazon_initial_50_30_10000\data.txt"
X, types, y = ToFormNumpy(path)
minmax_scale(X, copy=False)
_, ln = np.unique(y, return_counts=True)
w = Lagranj1(X, y)
compactness(X, y, types=types, metric=1)
while X.shape[1] > 2:
cond = w != w.min()
X = X[:, cond]
w = w[cond]
compactness(X, y, types=types, metric=1)
def main():
path = r"D:\Nuu\AI\Selections\LSVT_voice_rehabilitation\data.txt"
X, types, y = ToFormNumpy(path)
#minmax_scale(X, copy=False)
Normalizing_Estmation(X, y)
_, ln = np.unique(y, return_counts=True)
w = Lagranj1(X, y)
compactness(X, y, types=types, metric=1)
while X.shape[1] > 2:
cond = w != w.min()
X = X[:, cond]
w = w[cond]
compactness(X, y, types=types, metric=1)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\arcene_train.txt")
X_Test = np.loadtxt(
r"D:\Nuu\AI\Selections\Arcena Data Set\arcene_test.data")
minmax_scale(X, copy=False)
minmax_scale(X_Test, copy=False)
#Normalizing_Estmation(X, y, types=types)
nnc = NearestNeighborClassifier()
begin = time.time()
nnc.fit(X, y)
print(nnc.predict(X_Test))
end = time.time()
print("Time: ", (end - begin) * 1000)
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
X, types, y = ToFormNumpy(r"D:\Nuu\AI\Selections\Amazon_initial_50_30_10000\data.txt")
minmax_scale(X, copy=False)
#minmax_scale(X, copy=False)
X = SelectKBest(chi2, k=2000).fit_transform(X, y)
metric = 1
# nnc = NearestNeighborClassifier_(noisy=True)
nnc = NearestNeighborClassifier()
# nnc = TemplateClassifier(noisy=True)
nn = MLPClassifier()
svm = SVC()
k = 10
mean1 = 0
mean2 = 0
mean3 = 0
for i in range(k):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.5, random_state=None, shuffle=True)
#nnc.fit(X_train, y_train)
svm.fit(X_train, y_train)
nn.fit(X_train, y_train)
#mean1 += nnc.score(X_test, y_test)
mean2 += svm.score(X_test, y_test)
mean3 += nn.score(X_test, y_test)
mean1 /= k
mean2 /= k
mean3 /= k
print(mean1, mean2, mean3)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
minmax_scale(X, copy=False)
#X, types, y = ToFormNumpy(r"D:\Nuu\Data mining\Articles\PCA operator\Computing\Lagranj\Spame\data\own\(4595, 57).txt")
k = 5
k_fold = KFold(n_splits=k, shuffle=True, random_state=42)
mlp = MLPClassifier(hidden_layer_sizes=(50, 200),
activation='relu',
max_iter=1000,
alpha=1e-5,
solver='adam',
verbose=False,
tol=1e-4,
random_state=1,
learning_rate_init=.1)
max_mean = sum(CVS(mlp, X, y, cv=k_fold, n_jobs=4, scoring='accuracy')) / k
print('Score = ', max_mean)
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\german.txt")
_, ln = np.unique(y, return_counts=True)
for j in range(X.shape[1]):
if types[j] == 0:
gradation = {}
for i in range(X.shape[0]):
if not (X[i, j] in gradation):
nyu1 = np.count_nonzero(X[y == 0, j] == X[i, j]) / ln[0]
nyu2 = np.count_nonzero(X[y == 1, j] == X[i, j]) / ln[1]
gradation[X[i, j]] = nyu1 / (nyu1 + nyu2)
if y[i] == 0:
X[i, j] = gradation[X[i, j]]
else:
X[i, j] = 1 - gradation[X[i, j]]
for i in range(X.shape[0]):
for j in range(X.shape[1]):
print(X[i, j], end=' ')
print(y[i] + 1)
import numpy as np
from Test.read_data import ToFormNumpy
from uz.nuu.datamining.graphic.drawing import mscatter
from ai.own.fris import Fris
X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
#X = np.array([[4, 4], [3, 6], [5, 2]], dtype=float)
#X = np.array([[4, 3, 5], [4, 6, 2]])
y -= 1
X -= X.mean(axis=0)
covmat = np.dot(X.T, X)
covmat = np.array([[0.6066353658781276, 0.6301733805074563, 0.6214466136515134, 0.8592131106607721, 0.8736921033877233, 0.600853414661878, 0.6094070313817901, 0.5867057852210043, 0.5786615866051649, 0.7006998089403434, 0.6163119318278932, 0.6522341941050182, 0.600853414661878, 0.8736921033877233, 0.8096234318951467, 0.5791038997423555, 0.635994346239335, 0.6229420214572404, 0.5867057852210043, 0.600853414661878, 0.6214466136515134, 0.6453908994369276, 0.6522341941050182, 0.6066353658781276, 0.6013082124440698, 0.6203214350207669, 0.6340651713331743, 0.7392127093686114, 0.7446575751698247, ],
[0.6301733805074563, 0.2526354862657758, 0.5541910135822533, 0.903192279138827, 0.7186195103789759, 0.2818595878729509, 0.2742637217767136, 0.31516312154767984, 0.27900015828746344, 0.6846108915151231, 0.5030495974816688, 0.5251028311161942, 0.28179954267556345, 0.8668488087196328, 0.7817367478837411, 0.2877247700558762, 0.27703932588862057, 0.2869910742219577, 0.31516312154767984, 0.28445312424525493, 0.5219978701790134, 0.5378191108703357, 0.34730253638939607, 0.3516703786191537, 0.30316574926819917, 0.27619445726349956, 0.35664261143103015, 0.48890977094392085, 0.4597624350408315, ],
[0.6214466136515134, 0.5541910135822533, 0.5071687871984828, 0.8809462023270487, 0.8379863159373181, 0.4566814030065701, 0.41643789297835326, 0.48890977094392085, 0.5215768849770335, 0.6148852638830367, 0.5481133694942159, 0.5215768849770335, 0.44322496215591983, 0.8592131106607721, 0.8241024246220979, 0.4391580890467305, 0.5053005492500667, 0.4566814030065701, 0.48890977094392085, 0.4391580890467305, 0.5381905290591259, 0.5215768849770335, 0.5738029789254734, 0.45777269474374155, 0.4785228435562512, 0.4784405786632958, 0.5644675856257149, 0.600853414661878, 0.6301733805074563, ],
[0.8592131106607721, 0.903192279138827, 0.8809462023270487, 0.903192279138827, 0.9193229606741115, 0.9193229606741115, 0.845217674987534, 0.9349088509444856, 0.903192279138827, 0.8962343189514681, 0.903192279138827, 0.8719953806813495, 0.9429656085186894, 0.9349088509444856, 0.903192279138827, 0.9193229606741115, 0.9349088509444856, 0.9349088509444856, 0.9349088509444856, 0.9193229606741115, 0.903192279138827, 0.8719953806813495, 0.9349088509444856, 0.9116872626152508, 0.8668488087196328, 0.9116872626152508, 0.9349088509444856, 0.8574610244988864, 0.9193229606741115, ],
[0.8736921033877233, 0.7186195103789759, 0.8379863159373181, 0.9193229606741115, 0.7515372833858358, 0.7628688079319111, 0.6846108915151231, 0.7627978386701475, 0.7897935054579449, 0.8507181271843484, 0.8299295583631144, 0.8110616184112844, 0.7344496686738706, 0.8412934092221398, 0.9349088509444856, 0.7446575751698247, 0.7543028551937238, 0.770433536729008, 0.7627978386701475, 0.733098503105927, 0.8241024246220979, 0.8110616184112844, 0.772785926238042, 0.772785926238042, 0.7344496686738706, 0.7628688079319111, 0.772785926238042, 0.7543028551937238, 0.733098503105927, ],
[0.600853414661878, 0.2818595878729509, 0.4566814030065701, 0.9193229606741115, 0.7628688079319111, 0.2529438867226544, 0.2838028823181013, 0.3026134523536157, 0.2994060876020787, 0.6453908994369276, 0.4843688528914958, 0.45528877058201483, 0.26080178173719376, 0.8890029599012524, 0.8096234318951467, 0.2902945261519872, 0.27851214243196426, 0.3108877251416227, 0.3026134523536157, 0.28179954267556345, 0.505249644596341, 0.48512989604303636, 0.3287914492071879, 0.3088273128362215, 0.31642332755918506, 0.27897117607585314, 0.3488175629229823, 0.5943920163667752, 0.733098503105927, ],
[0.6094070313817901, 0.2742637217767136, 0.41643789297835326, 0.845217674987534, 0.6846108915151231, 0.2838028823181013, 0.26748329621380845, 0.26854783514026276, 0.28363552862439273, 0.6229420214572404, 0.4893443554913488, 0.5219978701790134, 0.2838028823181013, 0.8209787367174153, 0.8096234318951467, 0.29513492705771843, 0.28796613985923564, 0.284858355014257, 0.26854783514026276, 0.29183370452858204, 0.4736911373140029, 0.5335978145926178, 0.31272921027932166, 0.3649482444582667, 0.310192820883244, 0.2882702301410542, 0.3255433607326703, 0.4659357947405461, 0.4477305813608709, ],
[0.5867057852210043, 0.31516312154767984, 0.48890977094392085, 0.9349088509444856, 0.7627978386701475, 0.3026134523536157, 0.26854783514026276, 0.2756011393724831, 0.27851214243196426, 0.6439822896393053, 0.4784405786632958, 0.4937398422921809, 0.28727205304785114, 0.8890029599012524, 0.8096234318951467, 0.30932892803716855, 0.2778019296207863, 0.3009744711303732, 0.2815887156644395, 0.304426475473246, 0.4631916251812317, 0.509335021732943, 0.3907907164774277, 0.3461766889383816, 0.3653727967016757, 0.3211642971850841, 0.3711599498533426, 0.5266203099907628, 0.5378191108703357, ],
[0.5786615866051649, 0.27900015828746344, 0.5215768849770335, 0.903192279138827, 0.7897935054579449, 0.2994060876020787, 0.28363552862439273, 0.27851214243196426, 0.2502966961021898, 0.6292602179016433, 0.5365374282968938, 0.5095717097944269, 0.2940188889706336, 0.8719953806813495, 0.7970248645081608, 0.3168840704104253, 0.2778019296207863, 0.2934039458909541, 0.27851214243196426, 0.2988699166872887, 0.5365374282968938, 0.5095717097944269, 0.3402344437979182, 0.33814267125328745, 0.3034149962880475, 0.274536005939124, 0.35820631321744906, 0.48512989604303636, 0.4902953435989813, ],
[0.7006998089403434, 0.6846108915151231, 0.6148852638830367, 0.8962343189514681, 0.8507181271843484, 0.6453908994369276, 0.6229420214572404, 0.6439822896393053, 0.6292602179016433, 0.6268914794527267, 0.6301733805074563, 0.6229420214572404, 0.59789722907763, 0.8962343189514681, 0.8379863159373181, 0.6214466136515134, 0.6377552013780671, 0.6453908994369276, 0.6439822896393053, 0.6453908994369276, 0.6301733805074563, 0.6008630753990813, 0.6684802099798388, 0.6214466136515134, 0.6148852638830367, 0.6084891127207386, 0.6684802099798388, 0.7817367478837411, 0.727298345115717, ],
[0.6163119318278932, 0.5030495974816688, 0.5481133694942159, 0.903192279138827, 0.8299295583631144, 0.4843688528914958, 0.4893443554913488, 0.4784405786632958, 0.5365374282968938, 0.6301733805074563, 0.5239109933987439, 0.5365374282968938, 0.5136370223229911, 0.8890029599012524, 0.8096234318951467, 0.4841139580560516, 0.48512989604303636, 0.4736911373140029, 0.4784405786632958, 0.4784405786632958, 0.5239109933987439, 0.5492767451935974, 0.5365374282968938, 0.5483231561182563, 0.5263093085661757, 0.5596923575394251, 0.48512989604303636, 0.7627978386701475, 0.65905549201763, ],
[0.6522341941050182, 0.5251028311161942, 0.5215768849770335, 0.8719953806813495, 0.8110616184112844, 0.45528877058201483, 0.5219978701790134, 0.4937398422921809, 0.5095717097944269, 0.6229420214572404, 0.5365374282968938, 0.5139411126048097, 0.4621320652501053, 0.8809462023270487, 0.7917037304565143, 0.44322496215591983, 0.509335021732943, 0.4631916251812317, 0.4937398422921809, 0.4754180312457966, 0.5492767451935974, 0.5251028311161942, 0.5156610953938348, 0.4843688528914958, 0.4671480686328496, 0.505249644596341, 0.5263093085661757, 0.7173812268095862, 0.6439822896393053, ],
[0.600853414661878, 0.28179954267556345, 0.44322496215591983, 0.9429656085186894, 0.7344496686738706, 0.26080178173719376, 0.2838028823181013, 0.28727205304785114, 0.2940188889706336, 0.59789722907763, 0.5136370223229911, 0.4621320652501053, 0.2529438867226544, 0.8809462023270487, 0.7448781372315151, 0.27619445726349956, 0.29513492705771843, 0.2941543622315709, 0.28727205304785114, 0.2848552338530067, 0.5156610953938348, 0.4719695196309896, 0.3399748672205984, 0.311812703572169, 0.310192820883244, 0.262221761479371, 0.3461766889383816, 0.5738029789254734, 0.733098503105927, ],
[0.8736921033877233, 0.8668488087196328, 0.8592131106607721, 0.9349088509444856, 0.8412934092221398, 0.8890029599012524, 0.8209787367174153, 0.8890029599012524, 0.8719953806813495, 0.8962343189514681, 0.8890029599012524, 0.8809462023270487, 0.8809462023270487, 0.8890029599012524, 0.903192279138827, 0.8574610244988864, 0.9349088509444856, 0.8736921033877233, 0.8890029599012524, 0.8736921033877233, 0.8962343189514681, 0.8809462023270487, 0.9116872626152508, 0.8890029599012524, 0.8668488087196328, 0.8668488087196328, 0.8890029599012524, 0.8011284484187231, 0.8302166308847824, ],
def main():
X, types, y = ToFormNumpy(r"D:/test.txt")
mscatter1(X, y, marker=['v', 's'], size=6, colors=['black', 'black'])
def main():
X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\german.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
#X, types, y = ToFormNumpy(r"D:\Nuu\Data mining\Articles\PCA operator\Computing\Gastown1.txt")
y -= 1
#y[y == 2] = 1
_, ln = np.unique(y, return_counts=True)
#minmax_scale(X, copy=False)
Normalizing_Estmation(X, y, types=types)
#return None
root = r"D:\Nuu\Data mining\Articles\PCA operator\Computing\Lagranj"
selection_Name = r'\It_bori'
preproccesing_name = r'own'
img_path = root + selection_Name + \
"\images " + preproccesing_name
save_name = img_path + r"\img"
save_name += str(X.shape) + ".png"
path = root + selection_Name + \
"/res " + preproccesing_name + ".txt"
path1 = root + selection_Name + \
"/res1 " + preproccesing_name + ".txt"
p_res = root + selection_Name + \
"/data/" + preproccesing_name + "/"
p_res_PCA = root + selection_Name + \
"/data/" + preproccesing_name + "/"
file = open(path, 'w')
file1 = open(path1, 'w')
shape = X.shape
#Computing for X
print("Computing for shape " + str(X.shape))
file.write("Computing for shape " + str(X.shape) + "\n")
group, comp1, noisy1 = Fris(X, y, types=types, file=file)
similarity0 = DecomposionEstimation(group, group, X.shape[0])
print("Similarity between shape " + str(shape) + " and " + str(X.shape) +
" are " + str(similarity0))
file.write("Similarity between shape " + str(shape) + " and " +
str(X.shape) + " are " + str(similarity0) + "\n")
writeNP(p_res + str(X.shape) + ".txt", X, y, types=types)
#PCA
print("Computing for PCA")
file.write("Computing for PCA\n")
#pca = PCA(n_components=2)
pca = KernelPCA(n_components=2, kernel='poly')
pca.fit(X, y=y)
transform = pca.transform(X)
writeNP(p_res_PCA + str(transform.shape) + str(X.shape) + ".txt",
transform,
y,
types=[1, 1])
mscatter(transform, y=y, save_name=save_name)
group_b, comp2, noisy2 = Fris(transform, y, types, file=file)
similarity = DecomposionEstimation(group, group_b, X.shape[0])
print("Similarity between shape " + str(shape) + " and " +
str(transform.shape) + " are " + str(similarity))
file.write("Similarity between shape " + str(shape) + " and " +
str(transform.shape) + " are " + str(similarity) + "\n")
file1.write(
str(X.shape[1]) + "\t" + str(comp1) + "\t" + str(similarity0) + "\t" +
str(comp2) + "\t" + str(similarity) + "\t" + str(similarity) + "\t" +
str(noisy1) + "\t" + str(noisy2) + "\n")
# 25
w = Lagranj(X, y, types, ln=ln)
while X.shape[1] > 2:
cond = w != w.min()
X = X[:, cond]
types = types[cond]
w = w[cond]
print("\n" + "*" * 50)
file.write("\n" + "*" * 50 + "\n")
print("Computing for shape " + str(X.shape))
file.write("Computing for shape " + str(X.shape) + "\n")
# For X
group_b, comp1, noisy2 = Fris(X, y, types=types, file=file)
similarity0 = DecomposionEstimation(group,
group_b,
obj_count=X.shape[0])
print("Similarity between shape " + str(shape) + " and " +
str(X.shape) + " are " + str(similarity0))
file.write("Similarity between shape " + str(shape) + " and " +
str(X.shape) + " are " + str(similarity0) + "\n")
#PCA
print("Computing for PCA")
file.write("Computing for PCA\n")
pca = PCA(n_components=2)
pca.fit(X, y=y)
transform = pca.transform(X)
#Save images
save_name = img_path + r"\img"
save_name += str(X.shape) + ".png"
mscatter(transform, y=y, save_name=save_name)
group_c, comp2, noisy2 = Fris(transform, y, types, file=file)
similarity = DecomposionEstimation(group, group_c, X.shape[0])
similarity1 = DecomposionEstimation(group_b, group_c, X.shape[0])
print("Similarity between shape " + str(shape) + " and " +
str(transform.shape) + " are " + str(similarity))
file.write("Similarity between shape " + str(shape) + " and " +
str(transform.shape) + " are " + str(similarity) + "\n")
file1.write(
str(X.shape[1]) + "\t" + str(comp1) + "\t" + str(similarity0) +
"\t" + str(comp2) + "\t" + str(similarity) + "\t" +
str(similarity1) + "\t" + str(noisy1) + "\t" + str(noisy2) + "\n")
writeNP(p_res + str(X.shape) + ".txt", X, y, types=types)
writeNP(p_res_PCA + str(transform.shape) + str(X.shape) + ".txt",
transform,
y,
types=[1, 1])
file.close()
file1.close()
def main():
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\IT_BORI_42_6.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\giper_my.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\spame.txt")
X, types, y = ToFormNumpy("D:\\tanlanmalar\\gasterlogy1394.txt")
#X, types, y = ToFormNumpy("D:\\tanlanmalar\\MATBIO_MY.txt")
y[y == 2] = 1
_, ln = np.unique(y, return_counts=True)
#print(ln)
#minmax_scale(X, copy=False)
Normalizing_Estmation(X, y)
indx = clearNoisy(X, y)
X = X[indx]
y = y[indx]
#print(X.shape)
#print(y.shape)
#return None
selection_Name = r'\Gasterology2'
preproccesing_name = r'own'
path = r"D:\Nuu\Data mining\Articles\Cross Validation\Computing" + selection_Name + \
r"\res " + preproccesing_name + ".txt"
file = open(path, 'w')
# Cross Validation
k = 10
k_fold = KFold(n_splits=k, shuffle=True, random_state=None)
#Nerual network
mlp = MLPClassifier(hidden_layer_sizes=(100, 200), activation='logistic')
# Knn
n_neighbors = 2 * min(ln) - 3
# mertic Euclidean
knc = KNeighborsClassifier(n_neighbors=n_neighbors, p=2)
#SVM
svc = SVC(kernel="linear", degree=5)
# RDF
rdf = RandomForestClassifier(max_depth=1000)
#print("MLP")
max_mean1 = CVS(mlp, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
#print("KNN")
max_mean2 = CVS(knc, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
#print("SVM")
max_mean3 = CVS(svc, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
print(X.shape[1], max_mean1, max_mean2, max_mean3)
# 25
w = Lagranj(X, y, types)
while X.shape[1] > 2:
# Cross Validation
k = 5
k_fold = KFold(n_splits=k, shuffle=True, random_state=42)
# Nerual network
mlp = MLPClassifier(hidden_layer_sizes=(50, 200),
activation='relu',
max_iter=1000,
alpha=1e-5,
solver='adam',
verbose=False,
tol=1e-8,
random_state=1,
learning_rate_init=.1)
# Knn
n_neighbors = 2 * min(ln) - 3
# mertic Euclidean
knc = KNeighborsClassifier(n_neighbors=n_neighbors, p=2)
# SVM
svc = SVC(gamma='scale')
max_mean1 = sum(CVS(mlp, X, y, cv=k_fold, n_jobs=4,
scoring='accuracy')) / k
max_mean2 = sum(CVS(knc, X, y, cv=k_fold, n_jobs=4,
scoring='accuracy')) / k
max_mean3 = sum(CVS(svc, X, y, cv=k_fold, n_jobs=4,
scoring='accuracy')) / k
print(X.shape[1], max_mean1, max_mean2, max_mean3)
file.write(
str(X.shape[1]) + "\t" + str(max_mean1) + "\t" + str(max_mean2) +
"\t" + str(max_mean3) + "\n")
cond = w != w.min()
X = X[:, cond]
w = w[cond]
file.close()