Add static type hints checking (qiskit-community/qiskit-aqua#1020)

* Add static type hints checking

* run mypy on python 3.5

* a few more type hint fixes

Co-authored-by: Julien Gacon <jules.gacon@googlemail.com>

qiskit/aqua/components/neural_networks/discriminative_network.py

@@ -14,6 +14,7 @@
 
 """ Discriminative Quantum or Classical Neural Networks."""
 
+from typing import List
 from abc import ABC, abstractmethod
 
 
@@ -29,7 +30,7 @@ def __init__(self) -> None:
         super().__init__()
         self._num_parameters = 0
         self._num_qubits = 0
-        self._bounds = list()
+        self._bounds = list()  # type: List[object]
 
     @abstractmethod
     def set_seed(self, seed):

qiskit/aqua/components/neural_networks/numpy_discriminator.py

@@ -19,6 +19,7 @@
 https://towardsdatascience.com/lets-code-a-neural-network-in-plain-numpy-ae7e74410795
 """
 
+from typing import Dict, Any
 import os
 import logging
 import numpy as np
@@ -378,7 +379,8 @@ def gradient_function(params):
 
         return gradient_function
 
-    def train(self, data, weights, penalty=False, quantum_instance=None, shots=None):
+    def train(self, data, weights, penalty=False,
+              quantum_instance=None, shots=None) -> Dict[str, Any]:
         """
         Perform one training step w.r.t to the discriminator's parameters
 

qiskit/aqua/components/neural_networks/pytorch_discriminator.py

@@ -16,6 +16,7 @@
 PyTorch Discriminator Neural Network
 """
 
+from typing import Dict, Any
 import os
 import logging
 import numpy as np
@@ -63,7 +64,7 @@ def __init__(self, n_features: int = 1, n_out: int = 1) -> None:
         # discriminator network parameters.
         self._optimizer = optim.Adam(self._discriminator.parameters(), lr=1e-5, amsgrad=True)
 
-        self._ret = {}
+        self._ret = {}  # type: Dict[str, Any]
 
     def set_seed(self, seed: int):
         """
@@ -178,7 +179,8 @@ def gradient_penalty(self, x, lambda_=5., k=0.01, c=1.):
 
         return lambda_ * ((d_g.norm(p=2, dim=1) - k)**2).mean()
 
-    def train(self, data, weights, penalty=True, quantum_instance=None, shots=None):
+    def train(self, data, weights, penalty=True,
+              quantum_instance=None, shots=None) -> Dict[str, Any]:
         """
         Perform one training step w.r.t. to the discriminator's parameters