def train(self, input_train, target_train, copy=True):
"""
Trains network. PNN doesn't actually train, it just stores
input data and use it for prediction.
Parameters
----------
input_train : array-like (n_samples, n_features)
target_train : array-like (n_samples,)
Target variable should be vector or matrix
with one feature column.
copy : bool
If value equal to ``True`` than input matrices will
be copied. Defaults to ``True``.
Raises
------
ValueError
In case if something is wrong with input data.
"""
input_train = format_data(input_train, copy=copy)
target_train = format_data(target_train, copy=copy)
n_target_features = target_train.shape[1]
if n_target_features != 1:
raise ValueError("Target value must be one dimensional array")
LazyLearningMixin.train(self, input_train, target_train)
def train(self, input_train, target_train, copy=True):
"""
Trains network. PNN doesn't actually train, it just stores
input data and use it for prediction.
Parameters
----------
input_train : array-like (n_samples, n_features)
target_train : array-like (n_samples,)
Target variable should be vector or matrix
with one feature column.
copy : bool
If value equal to ``True`` than input matrices will
be copied. Defaults to ``True``.
Raises
------
ValueError
In case if something is wrong with input data.
"""
input_train = format_data(input_train, copy=copy)
target_train = format_data(target_train, copy=copy)
LazyLearningMixin.train(self, input_train, target_train)
n_target_features = target_train.shape[1]
if n_target_features != 1:
raise ValueError("Target value should be a vector or a "
"matrix with one column")
classes = self.classes = np.unique(target_train)
n_classes = classes.size
n_samples = input_train.shape[0]
row_comb_matrix = self.row_comb_matrix = np.zeros(
(n_classes, n_samples)
)
class_ratios = self.class_ratios = np.zeros(n_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)
