def main():
# X, types, y = ToFormNumpy("D:\\german.txt")
X, types, y = ToFormNumpy("D:\\german1.txt")
# X, types, y = ToFormNumpy("D:\\tanlanmalar\\german.txt")
#minmax_scale(X, copy=False)
#Normalizing_Estmation(X, y)
y -= 1
_, ln = np.unique(y, return_counts=True)
w = Lagranj1(X, y)
print(w)
res = compactness(X, y, types=types, metric=1)
print(res)
while X.shape[1] > 2:
cond = w != w.min()
X = X[:, cond]
w = w[cond]
res = compactness(X, y, types=types, metric=1)
print(res)
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():
path_train = r"D:\Nuu\AI\Selections\gene-expression\data_train.csv"
path_test = r"D:\Nuu\AI\Selections\gene-expression\data_test.csv"
X_train, types_train, y_train = ReadFromCSVWithHeaderClass(path_train)
X_test, types_test, y_test = ReadFromCSVWithHeaderClass(path_test)
#minmax_scale(X_train, copy=False)
#minmax_scale(X_test, copy=False)
Normalizing_Estmation(X_train, y_train, types_train)
print(X_train)
_, ln = np.unique(y_train, return_counts=True)
w = Lagranj1(X_train, y_train)
value = w.max()
cond = w == value
while len(cond[cond == True]) <= X_train.shape[1]:
value = np.max(w[w < value])
cond = w >= value
compactness(X_train[:, cond], y_train)
return 0
#cond = [356, 2266, 2358, 2641, 4049, 6280]
#cond = [356, 2266, 2358, 2641, 2724, 4049]
#cond = [356, 2266, 2641, 3772, 4049, 4261]
#cond = [4847]
#X_train = X_train[:, cond]
#X_test = X_test[:, cond]
nnc1 = NearestNeighborClassifier_(noisy=True)
nnc2 = NearestNeighborClassifier()
nnc3 = TemplateClassifier(noisy=True)
nn = MLPClassifier()
svm = SVC()
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)
print("NearestNeighborClassifier_", mean1)
print("NearestNeighborClassifier", mean2)
print("TemplateClassifier", mean3)
print("SVC", mean4)
print("MLPClassifier", 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 calculate(self):
try:
noisy_indx = find_noisy(self.X, self.y)
noisies = self.count(noisy_indx, self.y)
if self.cal > 0:
X = self.X[noisy_indx == False]
y = self.y[noisy_indx == False]
else:
X = self.X
y = self.y
shells = self.count(find_shell(X, y), y)
etalons = self.count(find_standard(X, y), y)
comp = compactness(X, y)
self.result.setRowCount(self.cal + 1)
self.result.setItem(self.cal, 0, QTableWidgetItem(str(shells[0])))
self.result.setItem(self.cal, 1, QTableWidgetItem(str(shells[1])))
self.result.setItem(self.cal, 2, QTableWidgetItem(str(noisies[0])))
self.result.setItem(self.cal, 3, QTableWidgetItem(str(noisies[1])))
self.result.setItem(self.cal, 4, QTableWidgetItem(str(etalons[0])))
self.result.setItem(self.cal, 5, QTableWidgetItem(str(etalons[1])))
self.result.setItem(self.cal, 6, QTableWidgetItem(str(comp[0])))
self.result.setItem(self.cal, 7, QTableWidgetItem(str(comp[1])))
self.result.setItem(self.cal, 8, QTableWidgetItem(str(comp[2])))
self.cal += 1
except Exception as exc:
QMessageBox.about(self, "Hisoblashda xatolik bor: ", str(exc))
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():
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():
path = r"D:\Nuu\AI\Selections\gene-expression\data1.csv"
X, types, y = ReadFromCSVWithHeaderClass(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():
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():
path = r"D:\Nuu\AI\Selections\leukemia\leukemia_big.csv"
X, types, y = ReadFromCSVWithHeaderClass(path)
#minmax_scale(X, copy=False)
Normalizing_Estmation(X, y)
_, ln = np.unique(y, return_counts=True)
w = Lagranj1(X, y)
while X.shape[1] > 13:
cond = w != w.min()
X = X[:, cond]
w = w[cond]
#print(X.shape)
compactness(X, y, types=types, metric=1)
"""nnc1 = NearestNeighborClassifier_(noisy=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():
#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)
unique, ln = np.unique(y, return_counts=True)
number_class = len(unique)
#return 0
value = w.max()
cond_opt = w == value
comp_opt = compactness(X[:, cond_opt], y, types)
print(len(cond_opt[cond_opt == True]), comp_opt, sep="\t")
while len(cond_opt[cond_opt == True]) < X.shape[1]:
value = np.max(w[w < value])
cond_current = np.logical_or(w == value, cond_opt)
comp_current = compactness(X[:, cond_current], y, types)
if comp_opt[number_class] < comp_current[number_class]:
cond_opt = cond_current
comp_opt = comp_current
print(len(cond_opt[cond_opt == True]), comp_opt, sep="\t")
return 0
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)
