feat(Models): added MAE & RMSE metrics, fixed NaN & Inf values in data,

utils/evaluate.py

@@ -0,0 +1,9 @@
+from math import sqrt
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+def print_metrics(y_true, y_pred):
+    rmse = sqrt(mean_squared_error(y_true, y_pred))
+    print("RMSE: %.2f" % rmse)
+
+    mae = mean_absolute_error(y_true, y_pred)
+    print("MAE: %.2f" % mae)

utils/rolling.py

@@ -0,0 +1,6 @@
+import numpy as np
+
+def rolling_window(a, window):
+    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
+    strides = a.strides + (a.strides[-1],)
+    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)