fixed class docs

mirapy/classifiers/models.py

@@ -60,8 +60,7 @@ def train(self, x_train, y_train, epochs, batch_size, reset_weights,
         :param epochs: Integer. Number of epochs during training.
         :param batch_size: Number of samples per gradient update.
         :param reset_weights: Boolean. Set true to reset the weights of model.
-        :param class_weight: Dictionary. Weights of classes in loss function
-        during training.
+        :param class_weight: Dictionary. Weights of classes in loss function.
         :param validation_data: Numpy array of validation data.
         :param verbose: Value is 0, 1, or 2.
         """
@@ -134,8 +133,7 @@ def train(self, x_train, y_train, epochs=50, batch_size=100,
         :param epochs: Integer. Number of epochs during training.
         :param batch_size: Number of samples per gradient update.
         :param reset_weights: Boolean. Set true to reset the weights of model.
-        :param class_weight: Dictionary. Weights of classes in loss function
-        during training.
+        :param class_weight: Dictionary. Weights of classes in loss function during training.
         :param validation_data: Numpy array of validation data.
         :param verbose: Value is 0, 1, or 2.
         """
@@ -160,7 +158,7 @@ def predict(self, x):
 
 class AtlasVarStarClassifier(Classifier):
     """
-    Classification model for ATLAS variable stars
+    Classification model for ATLAS variable stars.
 
     :param activation: String (activation function name).
     :param input_size: Integer. Dimension of Feature Vector.
@@ -202,8 +200,7 @@ def train(self, x_train, y_train, epochs=50, batch_size=100,
         :param epochs: Integer. Number of epochs during training.
         :param batch_size: Number of samples per gradient update.
         :param reset_weights: Boolean. Set true to reset the weights of model.
-        :param class_weight: Dictionary. Weights of classes in loss function
-        during training.
+        :param class_weight: Dictionary. Weights of classes in loss function during training.
         :param validation_data: Numpy array of validation data.
         :param verbose: Value is 0, 1, or 2.
         """
@@ -268,8 +265,7 @@ def train(self, x_train, y_train, epochs=50, batch_size=100,
         :param epochs: Integer. Number of epochs during training.
         :param batch_size: Number of samples per gradient update.
         :param reset_weights: Boolean. Set true to reset the weights of model.
-        :param class_weight: Dictionary. Weights of classes in loss function
-        during training.
+        :param class_weight: Dictionary. Weights of classes in loss function.
         :param validation_data: Numpy array of validation data.
         :param verbose: Value is 0, 1, or 2.
         """

mirapy/fitting/models.py

@@ -2,10 +2,10 @@
 
 
 class Model1D:
+    """
+    Base class for 1-D model.
+    """
     def __init__(self):
-        """
-        Base class for 1-D model.
-        """
         pass
 
     def __call__(self, x):
@@ -42,14 +42,14 @@ def get_params_as_array(self):
 
 
 class Gaussian1D(Model1D):
-    def __init__(self, amplitude=1., mean=0., stddev=1.):
-        """
-        One dimensional Gaussian model.
+    """
+    One dimensional Gaussian model.
 
-        :param amplitude: Amplitude.
-        :param mean: Mean.
-        :param stddev: Standard deviation.
-        """
+    :param amplitude: Amplitude.
+    :param mean: Mean.
+    :param stddev: Standard deviation.
+    """
+    def __init__(self, amplitude=1., mean=0., stddev=1.):
         self.amplitude = amplitude
         self.mean = mean
         self.stddev = stddev

mirapy/fitting/optimizers.py

@@ -4,16 +4,16 @@
 
 
 class ParameterEstimation:
+    """
+    Base class of parameter estimation of a model using regression.
+
+    :param x: Array of input values.
+    :param y: Array of target values.
+    :param model: Model instance.
+    :param loss_function: Instance of loss function.
+    :param callback: Callback function.
+    """
     def __init__(self, x, y, model, loss_function, callback=None):
-        """
-        Base class of parameter estimation of a model using regression.
-
-        :param x: Array of input values.
-        :param y: Array of target values.
-        :param model: Model instance.
-        :param loss_function: Instance of loss function.
-        :param callback: Callback function.
-        """
         self.x = x
         self.y = y
         self.init_model = deepcopy(model)