def train(self, input_train, target_train, copy=True):
input_train = format_data(input_train, copy=copy)
target_train = format_data(target_train, copy=copy)
if target_train.shape[1] != 1:
raise ValueError("Target value must be one dimentional array")
LazyLearning.train(self, input_train, target_train)
def train(self, input_train, target_train, copy=True):
input_train = format_data(input_train, copy=copy)
target_train = format_data(target_train, copy=copy)
if target_train.shape[1] != 1:
raise ValueError("Target value must be one dimentional array")
LazyLearning.train(self, input_train, target_train)
def train(self, input_train, target_train):
LazyLearning.train(self, input_train, target_train)
if target_train.ndim != 1:
raise ValueError("Target value must be in 1 dimention")
classes = self.classes = unique(target_train)
number_of_classes = classes.size
row_comb_matrix = self.row_comb_matrix = zeros(
(number_of_classes, input_train.shape[0])
)
class_ratios = self.class_ratios = zeros(number_of_classes)
for i, class_name in enumerate(classes):
class_val_positions = (target_train == i)
row_comb_matrix[i, class_val_positions] = 1
class_ratios[i] = np_sum(class_val_positions)
def train(self, input_train, target_train, copy=True):
input_train = format_data(input_train, copy=copy)
target_train = format_data(target_train, copy=copy)
LazyLearning.train(self, input_train, target_train)
if target_train.shape[1] != 1:
raise ValueError("Target value must be in 1 dimention")
classes = self.classes = unique(target_train)
number_of_classes = classes.size
row_comb_matrix = self.row_comb_matrix = zeros(
(number_of_classes, input_train.shape[0]))
class_ratios = self.class_ratios = zeros(number_of_classes)
for i, class_name in enumerate(classes):
class_val_positions = (target_train == i)
row_comb_matrix[i, class_val_positions.ravel()] = 1
class_ratios[i] = np_sum(class_val_positions)
def train(self, input_train, target_train, copy=True):
input_train = format_data(input_train, copy=copy)
target_train = format_data(target_train, copy=copy)
LazyLearning.train(self, input_train, target_train)
if target_train.shape[1] != 1:
raise ValueError("Target value must be in 1 dimension")
classes = self.classes = unique(target_train)
number_of_classes = classes.size
row_comb_matrix = self.row_comb_matrix = zeros(
(number_of_classes, input_train.shape[0])
)
class_ratios = self.class_ratios = zeros(number_of_classes)
for i, class_name in enumerate(classes):
class_val_positions = (target_train == i)
row_comb_matrix[i, class_val_positions.ravel()] = 1
class_ratios[i] = np_sum(class_val_positions)
def train(self, input_train, target_train):
if target_train.ndim != 1:
raise ValueError("Target value must be in 1 dimention")
LazyLearning.train(self, input_train, target_train)