Updating file parsing

main.py

@@ -14,50 +14,6 @@ inputs = np.array([[0, 0, 1, 0],
 # output data
 outputs = np.array([[0], [0], [0], [1], [1], [1]])
 
-# create NeuralNetwork class
-class NeuralNetwork:
-
-    # intialize variables in class
-    def __init__(self, inputs, outputs):
-        self.inputs  = inputs
-        self.outputs = outputs
-        # initialize weights as .50 for simplicity
-        self.weights = np.array([[.50], [.50], [.50], [0.50]])
-        self.error_history = []
-        self.epoch_list = []
-
-    #activation function ==> S(x) = 1/1+e^(-x)
-    def sigmoid(self, x, deriv=False):
-        if deriv == True:
-            return x * (1 - x)
-        return 1 / (1 + np.exp(-x))
-
-    # data will flow through the neural network.
-    def feed_forward(self):
-        self.hidden = self.sigmoid(np.dot(self.inputs, self.weights))
-
-    # going backwards through the network to update weights
-    def backpropagation(self):
-        self.error  = self.outputs - self.hidden
-        delta = self.error * self.sigmoid(self.hidden, deriv=True)
-        self.weights += np.dot(self.inputs.T, delta)
-
-    # train the neural net for 25,000 iterations
-    def train(self, epochs=25000):
-        for epoch in range(epochs):
-            # flow forward and produce an output
-            self.feed_forward()
-            # go back though the network to make corrections based on the output
-            self.backpropagation()    
-            # keep track of the error history over each epoch
-            self.error_history.append(np.average(np.abs(self.error)))
-            self.epoch_list.append(epoch)
-
-    # function to predict output on new and unseen input data                               
-    def predict(self, new_input):
-        prediction = self.sigmoid(np.dot(new_input, self.weights))
-        return prediction
-
 if __name__ == '__main__':
     build_db_path = "./data/sql/build_db.sql"
     fill_parks_path = "./data/sql/prefill_parks.sql"
@@ -73,14 +29,7 @@ if __name__ == '__main__':
     imp = Importer(db_conn)
     imp.parse_all_data("./data/stats/to_import", "./data/stats/imported/")
 
-    #we = Weather()
-    #print(we.get_weather(39.26733000, -76.79831000, "20250706", 12))
-
-    #park_data = db_conn.select("SELECT latitude, longitude FROM parks WHERE park_id = ?", ("ATL03",))
-    #print(get_sun_and_moon_phase(park_data[0], park_data[1], "20250709"))
-    #historic_weather = get_weather(park_data[0], park_data[1], game_stats["date"], hour)
-
-
+"""
 else:
     # create neural network   
     NN = NeuralNetwork(inputs, outputs)
@@ -100,4 +49,5 @@ else:
     plt.plot(NN.epoch_list, NN.error_history)
     plt.xlabel('Epoch')
     plt.ylabel('Error')
-    plt.savefig('plot.png')
+    plt.savefig('plot.png')
+"""