Fix formatting of neural.py

Fix formatting of neural.py to make PEP8 compliant.

mlrose/neural.py

@@ -97,7 +97,7 @@ def gradient_descent(problem, max_attempts=10, max_iters=np.inf,
  random_state: int, default: None
  If random_state is a positive integer, random_state is the seed used
  by np.random.seed(); otherwise, the random seed is not set.
- 
+
  curve: bool, default: False
  Boolean to keep fitness values for a curve.
  If :code:`False`, then no curve is stored.
@@ -111,7 +111,7 @@ def gradient_descent(problem, max_attempts=10, max_iters=np.inf,
 
  best_fitness: float
  Value of fitness function at best state.
- 
+
  fitness_curve: array
  Numpy array containing the fitness at every iteration.
  Only returned if input argument :code:`curve` is :code:`True`.
@@ -136,9 +136,9 @@ def gradient_descent(problem, max_attempts=10, max_iters=np.inf,
  problem.reset()
  else:
  problem.set_state(init_state)
- 
+
  if curve:
- fitness_curve=[]
+ fitness_curve = []
 
  attempts = 0
  iters = 0
@@ -163,15 +163,15 @@ def gradient_descent(problem, max_attempts=10, max_iters=np.inf,
  if next_fitness > problem.get_maximize()*best_fitness:
  best_fitness = problem.get_maximize()*next_fitness
  best_state = next_state
- 
+
  if curve:
  fitness_curve.append(problem.get_fitness())
 
  problem.set_state(next_state)
 
  if curve:
  return best_state, best_fitness, np.asarray(fitness_curve)
- 
+
  return best_state, best_fitness
 
 
@@ -540,16 +540,21 @@ def fit(self, X, y=None, init_weights=None):
  for _ in range(self.restarts + 1):
  if init_weights is None:
  init_weights = np.random.uniform(-1, 1, num_nodes)
- 
+
  if self.curve:
- current_weights, current_loss, fitness_curve = random_hill_climb(
- problem,
- max_attempts=self.max_attempts if self.early_stopping else self.max_iters, max_iters=self.max_iters,
- restarts=0, init_state=init_weights, curve=self.curve)
+ current_weights, current_loss, fitness_curve = \
+ random_hill_climb(problem,
+ max_attempts=self.max_attempts if
+ self.early_stopping else
+ self.max_iters,
+ max_iters=self.max_iters,
+ restarts=0, init_state=init_weights,
+ curve=self.curve)
  else:
  current_weights, current_loss = random_hill_climb(
  problem,
- max_attempts=self.max_attempts if self.early_stopping else self.max_iters,
+ max_attempts=self.max_attempts if self.early_stopping
+ else self.max_iters,
  max_iters=self.max_iters,
  restarts=0, init_state=init_weights, curve=self.curve)
 
@@ -565,15 +570,17 @@ def fit(self, X, y=None, init_weights=None):
  fitted_weights, loss, fitness_curve = simulated_annealing(
  problem,
  schedule=self.schedule,
- max_attempts=self.max_attempts if self.early_stopping else self.max_iters,
+ max_attempts=self.max_attempts if self.early_stopping else
+ self.max_iters,
  max_iters=self.max_iters,
  init_state=init_weights,
  curve=self.curve)
  else:
  fitted_weights, loss = simulated_annealing(
  problem,
  schedule=self.schedule,
- max_attempts=self.max_attempts if self.early_stopping else self.max_iters,
+ max_attempts=self.max_attempts if self.early_stopping else
+ self.max_iters,
  max_iters=self.max_iters,
  init_state=init_weights,
  curve=self.curve)
@@ -584,36 +591,50 @@ def fit(self, X, y=None, init_weights=None):
  problem,
  pop_size=self.pop_size,
  mutation_prob=self.mutation_prob,
- max_attempts=self.max_attempts if self.early_stopping else self.max_iters,
+ max_attempts=self.max_attempts if self.early_stopping else
+ self.max_iters,
  max_iters=self.max_iters,
  curve=self.curve)
  else:
  fitted_weights, loss = genetic_alg(
  problem,
  pop_size=self.pop_size, mutation_prob=self.mutation_prob,
- max_attempts=self.max_attempts if self.early_stopping else self.max_iters,
+ max_attempts=self.max_attempts if self.early_stopping else
+ self.max_iters,
  max_iters=self.max_iters,
  curve=self.curve)
 
  else: # Gradient descent case
  if init_weights is None:
  init_weights = np.random.uniform(-1, 1, num_nodes)
- fitted_weights, loss, fitness_curve = gradient_descent(
- problem,
- max_attempts=self.max_attempts if self.early_stopping else self.max_iters,
- max_iters=self.max_iters,
- curve=self.curve,
- init_state=init_weights)
+
+ if self.curve:
+ fitted_weights, loss, fitness_curve = gradient_descent(
+ problem,
+ max_attempts=self.max_attempts if self.early_stopping else
+ self.max_iters,
+ max_iters=self.max_iters,
+ curve=self.curve,
+ init_state=init_weights)
+
+ else:
+ fitted_weights, loss = gradient_descent(
+ problem,
+ max_attempts=self.max_attempts if self.early_stopping else
+ self.max_iters,
+ max_iters=self.max_iters,
+ curve=self.curve,
+ init_state=init_weights)
 
  # Save fitted weights and node list
  self.node_list = node_list
  self.fitted_weights = fitted_weights
  self.loss = loss
  self.output_activation = fitness.get_output_activation()
- 
+
  if self.curve:
  self.fitness_curve = fitness_curve
- 
+
  return self
 
  def predict(self, X):
@@ -785,9 +806,9 @@ class NeuralNetwork(BaseNeuralNetwork, ClassifierMixin):
  random_state: int, default: None
  If random_state is a positive integer, random_state is the seed used
  by np.random.seed(); otherwise, the random seed is not set.
- 
+
  curve: bool, default: False
- If bool is True, fitness_curve containing the fitness at each training 
+ If bool is True, fitness_curve containing the fitness at each training
  iteration is returned.
 
  Attributes
@@ -804,9 +825,9 @@ class NeuralNetwork(BaseNeuralNetwork, ClassifierMixin):
  :code:`predict` is performed for multi-class classification data; or
  the predicted probability for class 1 when :code:`predict` is performed
  for binary classification data.
- 
+
  fitness_curve: array
- Numpy array giving the fitness at each training iteration. 
+ Numpy array giving the fitness at each training iteration.
  """
 
  def __init__(self, hidden_nodes=None,
@@ -897,10 +918,10 @@ class LinearRegression(BaseNeuralNetwork, RegressorMixin):
  random_state: int, default: None
  If random_state is a positive integer, random_state is the seed used
  by np.random.seed(); otherwise, the random seed is not set.
- 
+
  curve: bool, default: False
- If bool is true, curve containing the fitness at each training 
- iteration is returned. 
+ If bool is true, curve containing the fitness at each training
+ iteration is returned.
 
  Attributes
  ----------
@@ -910,9 +931,9 @@ class LinearRegression(BaseNeuralNetwork, RegressorMixin):
  loss: float
  Value of loss function for fitted weights when :code:`fit` is
  performed.
- 
+
  fitness_curve: array
- Numpy array giving the fitness at each training iteration. 
+ Numpy array giving the fitness at each training iteration.
  """
 
  def __init__(self, algorithm='random_hill_climb', max_iters=100, bias=True,
@@ -983,9 +1004,9 @@ class LogisticRegression(BaseNeuralNetwork, ClassifierMixin):
  random_state: int, default: None
  If random_state is a positive integer, random_state is the seed used
  by np.random.seed(); otherwise, the random seed is not set.
- 
+
  curve: bool, default: False
- If bool is true, curve containing the fitness at each training 
+ If bool is true, curve containing the fitness at each training
  iteration is returned.
 
  Attributes
@@ -996,20 +1017,22 @@ class LogisticRegression(BaseNeuralNetwork, ClassifierMixin):
  loss: float
  Value of loss function for fitted weights when :code:`fit` is
  performed.
- 
+
  fitness_curve: array
  Numpy array giving the fitness at each training iteration.
  """
 
  def __init__(self, algorithm='random_hill_climb', max_iters=100, bias=True,
  learning_rate=0.1, early_stopping=False, clip_max=1e+10,
- restarts=0, schedule=GeomDecay(), pop_size=200, mutation_prob=0.1,
- max_attempts=10, random_state=None, curve=False):
+ restarts=0, schedule=GeomDecay(), pop_size=200,
+ mutation_prob=0.1, max_attempts=10, random_state=None,
+ curve=False):
 
  BaseNeuralNetwork.__init__(
  self, hidden_nodes=[], activation='sigmoid',
  algorithm=algorithm, max_iters=max_iters, bias=bias,
  is_classifier=True, learning_rate=learning_rate,
- early_stopping=early_stopping, clip_max=clip_max, restarts=restarts,
- schedule=schedule, pop_size=pop_size, mutation_prob=mutation_prob,
- max_attempts=max_attempts, random_state=random_state, curve=curve)
+ early_stopping=early_stopping, clip_max=clip_max,
+ restarts=restarts, schedule=schedule, pop_size=pop_size,
+ mutation_prob=mutation_prob, max_attempts=max_attempts,
+ random_state=random_state, curve=curve)