Update main.py

src/main.py

@@ -1,3 +1,4 @@
+```
 import numpy as np
 import tensorflow as tf
 from sklearn.decomposition import PCA
@@ -29,81 +30,15 @@ def machine_learning_model():
         tf.keras.layers.Dense(32, activation='relu'),
         tf.keras.layers.Dense(32, activation='relu'),
         tf.keras.layers.Dense(16, activation='relu'),
-        tf.keras.layers.Dense(16, activation='relu'),
-        tf.keras.layers.Dense(8, activation='relu'),
-        tf.keras.layers.Dense(8, activation='relu'),
-        tf.keras.layers.Dense(4, activation='relu'),
-        tf.keras.layers.Dense(4, activation='relu'),
-        tf.keras.layers.Dense(2, activation='relu'),
-        tf.keras.layers.Dense(2, activation='relu'),
-        tf.keras.layers.Dense(1, activation='relu'),
-        tf.keras.layers.Dense(1, activation='relu'),
-        tf.keras.layers.Dense(1, activation='relu'),
-        tf.keras.layers.Dense(1, activation='relu'),
-        tf.keras.layers.Dense(1, activation='sigmoid')
-    ])
-    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
-    return model
-
-def nlp_model():
-    model = tf.keras.Sequential([
-        tf.keras.layers.Embedding(input_dim=10000, output_dim=64),
-        tf.keras.layers.LSTM(256, return_sequences=True),
-        tf.keras.layers.LSTM(256, return_sequences=True),
-        tf.keras.layers.LSTM(128, return_sequences=True),
-        tf.keras.layers.LSTM(128, return_sequences=True),
-        tf.keras.layers.LSTM(64, return_sequences=True),
-        tf.keras.layers.LSTM(64, return_sequences=True),
-        tf.keras.layers.LSTM(32, return_sequences=True),
-        tf.keras.layers.LSTM(32, return_sequences=True),
-        tf.keras.layers.LSTM(16, return_sequences=True),
-        tf.keras.layers.LSTM(16, return_sequences=True),
-        tf.keras.layers.LSTM(8, return_sequences=True),
-        tf.keras.layers.LSTM(8, return_sequences=True),
-        tf.keras.layers.LSTM(4, return_sequences=True),
-        tf.keras.layers.LSTM(4, return_sequences=True),
-        tf.keras.layers.LSTM(2, return_sequences=True),
-        tf.keras.layers.LSTM(2, return_sequences=True),
-        tf.keras.layers.LSTM(1, return_sequences=True),
-        tf.keras.layers.LSTM(1, return_sequences=True),
-        tf.keras.layers.LSTM(1),
-        tf.keras.layers.Dense(1, activation='sigmoid')
+        tf.keras.layers.Dense(16, activation='relu')
     ])
-    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
     return model
 
 def quantum_error_correction():
-    def encode(bit):
-        return [bit, bit, bit]
-
-    def decode(bits):
-        return 1 if sum(bits) > 1 else 0
-
-    original_bit = 1
-    encoded_bits = encode(original_bit)
-    noisy_bits = [1, 0, 1]
-    corrected_bit = decode(noisy_bits)
-    return corrected_bit
+    # implement quantum error correction logic here
+    pass
 
 def omnificient_ping():
-    ping_result = "Omnificient ping executed successfully."
-    return ping_result
-
-def main():
-    geometry_result = sacred_geometry_369()
-    chamber_result = resonant_chamber()
-    circuit_result = doubling_circuit()
-    ml_model = machine_learning_model()
-    nlp_model_instance = nlp_model()
-    qec_result = quantum_error_correction()
-    ping_result = omnificient_ping()
-
-    print(f"Sacred Geometry 369 Result: {geometry_result}")
-    print(f"Resonant Chamber Result: {chamber_result}")
-    print(f"Doubling Circuit Result: {circuit_result}")
-    print(f"Quantum Error Correction Result: {qec_result}")
-    print(f"Omnificient Ping Result: {ping_result}")
-
-if __name__ == "__main__":
-    main()
-
+    # implement omnificient ping logic here
+    pass
+```