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")
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 = 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 fit(self, X, y):
# Check that X and y have correct shape
X, y = check_X_y(X, y)
# Store the classes seen during fit
self.classes_ = unique_labels(y)
self.X_ = X
self.y_ = y
if self.Is_noisy:
self.noisy = find_noisy(X, y, metric=self.metric)
# Return the classifier
return self
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():
path = r"D:\Nuu\AI\Selections\leukemia\leukemia_small.csv"
X, types, y = ReadFromCSVWithHeaderClass(path)
minmax_scale(X, copy=False)
#minmax_scale(X, copy=False)
"""w = Lagranj_nd(X)
value = w.max()
cond = w == value
while len(cond[cond == True]) < 661:
value = np.max(w[w < value])
cond = w >= value
print(len(cond[cond == True]))
X_Test = X[:, w >= value]
types_Test = types[w >= value]
metric = 1
noisy = find_noisy(X_Test, y, types=types_Test, metric=metric)
cond = np.logical_not(noisy)
X_Test = X_Test[cond]
y_Test = y[cond]
print(X.shape)
"""
noisy = find_noisy(X, y, types=types)
cond = np.logical_not(noisy)
X = X[cond]
y = y[cond]
k = 10
k_fold = KFold(n_splits=k, shuffle=True, random_state=None)
"""
# Neighbors
nnc = NearestNeighborClassifier()
nnc_ = NearestNeighborClassifier_()
knc = KNeighborsClassifier(n_neighbors=30)
begin = time.time()
max_mean1 = 0
#max_mean1 = CVS(nnc, X_Test, y_Test, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
end = time.time()
print("Time: ", (end - begin) * 1000)
max_mean2 = 0
max_mean2 = CVS(nnc_, X_Test, y_Test, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
begin = time.time()
max_mean3 = 0
max_mean3 = CVS(knc, X_Test, y_Test, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
end = time.time()
print("Time: ", (end - begin) * 1000)
print(max_mean1, max_mean2, max_mean3)
"""
nnc = NearestNeighborClassifier_()
nnc.fit(X, y)
svm = SVC(kernel="linear")
#svm.fit(X, y)
nn = MLPClassifier(hidden_layer_sizes=(100, 200))
#nn.fit(X, y)
max_mean = CVS(nnc, X, y, cv=k_fold, n_jobs=4, scoring='accuracy').mean()
print(max_mean)