def create_non_homogeneus_cloud(self, symbolClasses):
indicator = 1.0
ratio = global_v.N_LEARNING/(global_v.N_LEARNING+global_v.N_TEST)
for cl in symbolClasses:
for i in range(0, int(indicator)):
# instance of new symbol
distortedClass = SymbolClass(cl.name, cl.color)
# randomize position around a given class(based on position)
distortedClass.characteristicsValues = self.__generate_distortion(
cl.characteristicsValues[:],
global_v.HOMO_STD_DEV)
scope = int((global_v.N_LEARNING + global_v.N_TEST)/int(indicator))
for j in range (0, scope):
cloud_point = SymbolClass(cl.name, cl.color)
cloud_point.characteristicsValues = self.__generate_distortion(
distortedClass.characteristicsValues[:],
global_v.NON_HOMO_STD_DEV)
# store result
if(j < ratio * scope):
cl.learning_set.append(cloud_point)
else:
cl.test_set.append(cloud_point)
# Info about number of created points
console.write_non_homo(cl.name, int(indicator), "Symbol Class", "Groups")
console.write_point_text_number(">> Number of distorted classes per symbol", len(cl.learning_set) + len(cl.test_set))
console.write_point_text_number(">> Number of learning set points",
len(cl.learning_set))
console.write_point_text_number(">> Number of test set points",
len(cl.test_set))
indicator += 0.5
def create_homogeneus_cloud(self, symbolClasses):
for cl in symbolClasses:
# Learning set
for i in range(0, global_v.N_LEARNING + global_v.N_TEST):
# instance of new symbol
distortedClass = SymbolClass(cl.name, cl.color, type = SymbolType.NATIVE_LEARNING)
# randomize position around a given class(based on position)
distortedClass.characteristicsValues = self.__generate_distortion(
cl.characteristicsValues[:],
global_v.HOMO_STD_DEV)
# store result
if(i < global_v.N_LEARNING):
cl.learning_set.append(distortedClass)
else:
cl.test_set.append(distortedClass)
print(cl.name)
console.write_point_text_number(">> Number of learning set points",
len(cl.learning_set))
console.write_point_text_number(">> Number of test set points",
len(cl.test_set))
def create_homogeneus_cloud(self, symbolClasses):
for cl in symbolClasses:
# Learning set
for i in range(0, global_v.N_LEARNING + global_v.N_TEST):
# instance of new symbol
distortedClass = SymbolClass(cl.name, cl.color, type = SymbolType.NATIVE_LEARNING)
# randomize position around a given class(based on position)
distortedClass.characteristicsValues = self.__generate_distortion(
cl.characteristicsValues[:],
global_v.HOMO_STD_DEV)
# store result
if(i < global_v.N_LEARNING):
cl.learning_set.append(distortedClass)
else:
cl.test_set.append(distortedClass)
print(cl.name)
console.write_point_text_number(">> Number of learning set points",
len(cl.learning_set))
console.write_point_text_number(">> Number of test set points",
len(cl.test_set))
def create_non_homogeneus_cloud(self, symbolClasses):
indicator = 1.0
ratio = global_v.N_LEARNING/(global_v.N_LEARNING+global_v.N_TEST)
for cl in symbolClasses:
for i in range(0, int(indicator)):
# instance of new symbol
distortedClass = SymbolClass(cl.name, cl.color)
# randomize position around a given class(based on position)
distortedClass.characteristicsValues = self.__generate_distortion(
cl.characteristicsValues[:],
global_v.HOMO_STD_DEV)
scope = int((global_v.N_LEARNING + global_v.N_TEST)/int(indicator))
for j in range (0, scope):
cloud_point = SymbolClass(cl.name, cl.color)
cloud_point.characteristicsValues = self.__generate_distortion(
distortedClass.characteristicsValues[:],
global_v.NON_HOMO_STD_DEV)
# store result
if(j < ratio * scope):
cl.learning_set.append(cloud_point)
else:
cl.test_set.append(cloud_point)
# Info about number of created points
console.write_non_homo(cl.name, int(indicator), "Symbol Class", "Groups")
console.write_point_text_number(">> Number of distorted classes per symbol", len(cl.learning_set) + len(cl.test_set))
console.write_point_text_number(">> Number of learning set points",
len(cl.learning_set))
console.write_point_text_number(">> Number of test set points",
len(cl.test_set))
indicator += 0.5