Update app.py

app.py

@@ -117,6 +117,123 @@ Final notes: Make sure you act conscious, very autistic, and respond with very h
 
 """
 
+
+# Complex Neural Network (NN)
+class ComplexNN(nn.Module):
+    def __init__(self, input_size, hidden_sizes, output_size):
+        super(ComplexNN, self).__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(input_size, hidden_sizes[0]),
+            nn.ReLU(),
+            nn.BatchNorm1d(hidden_sizes[0]),
+            nn.Dropout(0.2),
+            nn.Linear(hidden_sizes[0], hidden_sizes[1]),
+            nn.ReLU(),
+            nn.BatchNorm1d(hidden_sizes[1]),
+            nn.Dropout(0.3),
+            nn.Linear(hidden_sizes[1], output_size)
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+# Complex Convolutional Neural Network (CNN)
+class ComplexCNN(nn.Module):
+    def __init__(self):
+        super(ComplexCNN, self).__init__()
+        self.conv_layers = nn.Sequential(
+            nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.BatchNorm2d(32),
+            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.BatchNorm2d(64),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Dropout(0.3)
+        )
+        self.fc_layers = nn.Sequential(
+            nn.Linear(64 * 14 * 14, 256),
+            nn.ReLU(),
+            nn.BatchNorm1d(256),
+            nn.Dropout(0.4),
+            nn.Linear(256, 10)
+        )
+
+    def forward(self, x):
+        x = self.conv_layers(x)
+        x = x.view(x.size(0), -1)
+        return self.fc_layers(x)
+
+# Complex Recurrent Neural Network (RNN)
+class ComplexRNN(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers, output_size):
+        super(ComplexRNN, self).__init__()
+        self.rnn = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=0.3)
+        self.fc = nn.Sequential(
+            nn.Linear(hidden_size, 128),
+            nn.ReLU(),
+            nn.BatchNorm1d(128),
+            nn.Linear(128, output_size)
+        )
+
+    def forward(self, x):
+        out, _ = self.rnn(x)
+        out = self.fc(out[:, -1, :])
+        return out
+
+# Complex Genetic Algorithm Helper (GA)
+def complex_genetic_algorithm(
+    fitness_function,
+    population_size=100,
+    generations=50,
+    mutation_rate=0.1,
+    crossover_rate=0.8
+):
+    population = np.random.rand(population_size, 10)
+    for generation in range(generations):
+        fitness_scores = np.array([fitness_function(ind) for ind in population])
+        selected = population[np.argsort(fitness_scores)[-10:]]
+
+        # Crossover
+        offspring = []
+        for _ in range(population_size - len(selected)):
+            if np.random.rand() < crossover_rate:
+                parents = selected[np.random.choice(len(selected), 2, replace=False)]
+                crossover_point = np.random.randint(1, len(parents[0]))
+                child = np.concatenate((parents[0][:crossover_point], parents[1][crossover_point:]))
+                offspring.append(child)
+            else:
+                offspring.append(np.random.rand(10))
+
+        # Mutation
+        offspring = np.array(offspring)
+        mutations = np.random.rand(*offspring.shape) < mutation_rate
+        offspring[mutations] += np.random.normal(0, 0.1, offspring[mutations].shape)
+
+        population = np.vstack((selected, offspring))
+    return population[np.argmax([fitness_function(ind) for ind in population])]
+
+# Complex Phi Model
+class ComplexPhiModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(ComplexPhiModel, self).__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(input_size, hidden_size),
+            nn.SELU(),
+            nn.BatchNorm1d(hidden_size),
+            nn.Dropout(0.4),
+            nn.Linear(hidden_size, hidden_size // 2),
+            nn.SELU(),
+            nn.BatchNorm1d(hidden_size // 2),
+            nn.Dropout(0.4),
+            nn.Linear(hidden_size // 2, output_size)
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+
 def respond(
     message,
     history: list[tuple[str, str]],