main.py

@@ -26,6 +26,8 @@ if __name__ == '__main__':
     for game in all_games:
         game_result, training_data = pull_training_data(db_conn, str(game[0]), game[1], game[2])
 
+    
+
     """
     build_db_path = "./data/sql/build_db.sql"
     fill_parks_path = "./data/sql/prefill_parks.sql"

neuralnet/nerulnetdata.py

@@ -0,0 +1,160 @@
+"""
+Data to use:
+    Previous Game 2
+        game_date
+        day_of_week
+        day_night
+        park
+        length_in_minutes
+        score
+        line_score
+        at_bats
+        hits
+        doubles
+        triples
+        homeruns
+        rbis
+        sacrifice_hits
+        sacrifice_flies
+        hit_by_pitch
+        walks
+        intentional_walks
+        strikeouts
+        stolen_bases
+        caught_stealing
+        grounded_double
+        interference
+        left_on_base
+        pitchers_used
+        individual_earned_runs
+        earned_runs
+        wild_pitches
+        assits
+        errors
+        double_play
+        triple_play
+        starting_1_id
+        starting_1_position
+        starting_2_id
+        starting_2_position
+        starting_3_id
+        starting_3_position
+        starting_4_id
+        starting_4_position
+        starting_5_id
+        starting_5_position
+        starting_6_id
+        starting_6_position
+        starting_7_id
+        starting_7_position
+        starting_8_id
+        starting_8_position
+        starting_9_id
+        starting_9_position
+
+    Previous Game 1
+        game_date
+        day_of_week
+        day_night
+        park
+        length_in_minutes
+        score
+        line_score
+        at_bats
+        hits
+        doubles
+        triples
+        homeruns
+        rbis
+        sacrifice_hits
+        sacrifice_flies
+        hit_by_pitch
+        walks
+        intentional_walks
+        strikeouts
+        stolen_bases
+        caught_stealing
+        grounded_double
+        interference
+        left_on_base
+        pitchers_used
+        individual_earned_runs
+        earned_runs
+        wild_pitches
+        assits
+        errors
+        double_play
+        triple_play
+        starting_1_id
+        starting_1_position
+        starting_2_id
+        starting_2_position
+        starting_3_id
+        starting_3_position
+        starting_4_id
+        starting_4_position
+        starting_5_id
+        starting_5_position
+        starting_6_id
+        starting_6_position
+        starting_7_id
+        starting_7_position
+        starting_8_id
+        starting_8_position
+        starting_9_id
+        starting_9_position
+
+    Predicted Game
+        game_date
+        day_of_week
+        day_night
+        park_id
+        home_plate_ump_id
+        b1_ump_id
+        b2_ump_id
+        b3_ump_id
+        lf_fence_distance
+        lf_fence_height
+        ct_fence_distance
+        ct_fence_height
+        rf_fence_distance
+        rf_fence_height
+        has_roof
+        latitude
+        longitude
+        elevation
+        starting_1_id
+        starting_1_position
+        starting_2_id
+        starting_2_position
+        starting_3_id
+        starting_3_position
+        starting_4_id
+        starting_4_position
+        starting_5_id
+        starting_5_position
+        starting_6_id
+        starting_6_position
+        starting_7_id
+        starting_7_position
+        starting_8_id
+        starting_8_position
+        starting_9_id
+        starting_9_position
+        temperature
+        humidity
+        dew_point
+        apparent_temperature
+        air_pressure
+        percipitation
+        rain
+        snowfall
+        cloud_cover
+        wind_speed
+        wind_direction
+        wind_gusts
+        sun_rise
+        sun_set
+        moon_phase
+
+"""

neuralnet/neuralnetwork.py

@@ -0,0 +1,26 @@
+import numpy as np
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+
+# create NeuralNetwork class
+class NeuralNetwork:
+    def __init__(self, inputs_len: int):
+        # Setup checkpoint
+        self.checkpoint_path = "./training/cp.ckpt"
+        self.cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1)
+
+        # Setup model
+        self.model = Sequential()
+        self.model.add(Dense(12, input_shape=(inputs_len,), activation='relu'))
+        self.model.add(Dense(8, activation='relu'))
+        self.model.add(Dense(1, activation='sigmoid'))
+        self.model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+        if os.path.isfile(self.checkpoint_path):
+            self.model.load_weights(self.checkpoint_path)
+
+    def train(inputs :list, outputs :list):
+        self.model.fit(inputs, outputs, epochs=150, batch_size=10, callbacks=[self.cp_callback])
+
+    def predict(self, new_input):
+        return self.model.predict(new_input)