Initial setup for training

main.py

@@ -1,11 +1,16 @@
 import numpy as np # helps with the math
 import matplotlib.pyplot as plt # to plot error during training
+import tensorflow as tf
 
-from data.get_data import pull_training_data
+from rich.progress import Progress
+
+from data.get_data import pull_training_data, normalize_training_data
 from data.db_connect import Database
 from data.build_weather import get_weather, get_sun_and_moon_phase
 from data.stats_importer import Importer
 
+from neuralnet.neuralnetwork import NeuralNetwork
+
 # input data
 inputs = np.array([[0, 0, 1, 0],
                    [0, 0, 1, 1],
@@ -23,13 +28,54 @@ if __name__ == '__main__':
     query = "SELECT game_date, game_number, park_id FROM games ORDER BY game_date"
     all_games = db_conn.selectall(query)
 
-    for game in all_games:
-        game_result, training_data = pull_training_data(db_conn, str(game[0]), game[1], game[2])
-
+    compiled_training_data = None
+    compiled_training_results = None
     
+    with Progress() as p_bar:
+        p_bar_val = p_bar.add_task("Getting Data ...", total=len(all_games))
+        for game in all_games:
+            p_bar.update(p_bar_val, advance=1)
+            game_result, training_data = pull_training_data(db_conn, str(game[0]), game[1], game[2])
+
+            training_data = normalize_training_data(training_data)
+
+            if compiled_training_results is None:
+                compiled_training_data = np.array([training_data])
+                compiled_training_results = np.array([game_result])
+            else:
+                compiled_training_data = np.append(compiled_training_data, np.array([training_data]), axis=0)
+                compiled_training_results = np.append(compiled_training_results, np.array([game_result]), axis=0)
+
+    ctd_len = len(compiled_training_data)
+    ctr_len = len(compiled_training_results)
+
+    td = compiled_training_data[:ctd_len-100]
+    tdt = compiled_training_data[ctd_len-100:]
+
+    tr = compiled_training_results[:ctr_len-100]
+    trt = compiled_training_results[ctr_len-100:]
+
+    nn = NeuralNetwork(len(compiled_training_data[0]))
+    nn.train(td, tr)
+    nn.summary()
+    predictions = nn.predict(np.array(tdt))
+
+    total_num = 0
+    accurate_num = 0
+    for pred, act in zip(predictions, trt):
+        total_num += 1
+        pred = round(pred[0] * 1000) / 1000.0
+        guess = round(pred)
+        print(f"Pred: {pred} -> Gue: {guess} -> Res: {act} -> Cor: {guess == act}")
+
+        accurate_num += int(guess==act)
+
+    print(f"Total: {total_num} -> Accu: {accurate_num} -> Perc: {accurate_num/total_num}")
+    #for index in range(len(trt)):
+    #    print(f"pred: {nn.predict(np.array([tdt[index]]))} : act : {trt[index]}")
 
     """
-    build_db_path = "./data/sql/build_db.sql"
+    build_db_path = "./data/sql/build_db .sql"
     fill_parks_path = "./data/sql/prefill_parks.sql"
     fill_teams_path = "./data/sql/prefill_teams.sql"