Implemented 'get_cells()' for 'ArtificialNeuralNetwork'.

Troubled_Cell_Detector.py

@@ -6,7 +6,7 @@ TODO: Adapt 'train()' to fit style
 TODO: Add ANN testing from Soraya to ANN implementation
 TODO: Add ANN classification from Soraya to ANN implementation
 TODO: Move ANN implementation to new file -> Done (Artificial_Neural_Network)
-TODO: Add ANN detection from Soraya to ANN
+TODO: Implement 'get_cells()' for 'ArtificialNeuralNetwork' -> Done
 TODO: Adapt calculate_approximate_solution() to not require a quadrature -> Done
 TODO: Add function to determine cell average and reconstructions -> Done
 TODO: Fix cell averages and reconstructions to create data with an x-point stencil
@@ -16,6 +16,7 @@ import os
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
+import torch
 from sympy import Symbol
 
 x = Symbol('x')
@@ -62,8 +63,8 @@ class TroubledCellDetector(object):
         cell_averages = self._calculate_approximate_solution(projection, [0], 0)
         left_reconstructions = self._calculate_approximate_solution(projection, [-1], self._polynomial_degree)
         right_reconstructions = self._calculate_approximate_solution(projection, [1], self._polynomial_degree)
-        return np.array(list(zip(cell_averages[:, 0], left_reconstructions[:, 1], cell_averages[:, 1],
-                                 right_reconstructions[:, 1], cell_averages[:, 2])))
+        return np.array(list(map(np.float64, zip(cell_averages[:, 0], left_reconstructions[:, 1], cell_averages[:, 1],
+                                 right_reconstructions[:, 1], cell_averages[:, 2]))))
 
     def plot_results(self, projection, troubled_cell_history, time_history):
         self._plot_shock_tube(troubled_cell_history, time_history)
@@ -201,6 +202,9 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
     def _reset(self, config):
         super()._reset(config)
 
+        self._stencil_len = config.pop('stencil_len', 3)
+        self._model = config.pop('model')
+        self._model_state = config.pop('model_state', 'Train24k24k_Valid8k8k_Norm12ReLU10nodesAdamlr1e-2MSE.pt')
         # training_dir = config.pop('data_dir', 'data')
         # training_file = config.pop('training_set', 'smooth_0.01k__troubled_0.01k__normalized.npy')
         # validation_file = config.pop('validation_set', 'smooth_0.01k__troubled_0.01k__normalized.npy')
@@ -209,7 +213,21 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
         # self._training_data = {'train': [], 'validation': [], 'test': []}
 
     def get_cells(self, projection):
-        pass
+        num_ghost_cells = self._stencil_len//2
+
+        projection = projection[:, 1:-1]
+        # projection = projection[:, :5]
+        projection = np.concatenate((projection[:, -num_ghost_cells:], projection, projection[:, :num_ghost_cells]),
+                                    axis=1)
+        input_data = torch.from_numpy(np.vstack([self.calculate_cell_average_and_reconstructions(
+            projection[:, cell-num_ghost_cells:cell+num_ghost_cells+1])
+            for cell in range(num_ghost_cells, len(projection[0])-num_ghost_cells)]))
+
+        self._model.load_state_dict(torch.load(self._model_state))
+        self._model.eval()
+
+        model_output = torch.round(self._model(input_data.float()))
+        return [cell for cell in range(len(model_output)) if model_output[cell, 0] == torch.tensor([1])]
 
 
 class WaveletDetector(TroubledCellDetector):