Rename train_once to compute_gradients, clean up normalize, document normalization in README

1 files changed, 7 insertions, 5 deletions

diff --git a/train.py b/train.py
index a9c865b..8b15547 100644
--- a/train.py
+++ b/train.py
@@ -8,7 +8,7 @@ THETAS_FILE = "thetas.csv"
 def normalize(data):
     min_val = min(data)
     max_val = max(data)
-    return [(x - min_val) / (max_val - min_val) for x in data], min_val, max_val
+    return [(x - min_val) / (max_val - min_val) for x in data]
 
 
 def load_data():
@@ -28,7 +28,7 @@ def estimate_price(mileage, theta0, theta1):
 
 
 # DV: dependant variable, IV: independant variable
-def train_once(learning_rate, DV, IV, theta0, theta1):
+def compute_gradients(learning_rate, DV, IV, theta0, theta1):
     tmp0 = (
         learning_rate
         * (1.0 / len(DV))
@@ -52,12 +52,14 @@ def denormalize_thetas(t0, t1, km_min, km_max, price_min, price_max):
 
 def train(learning_rate, iterations):
     kms, prices = load_data()
-    kms_norm, km_min, km_max = normalize(kms)
-    prices_norm, price_min, price_max = normalize(prices)
+    km_min, km_max = min(kms), max(kms)
+    price_min, price_max = min(prices), max(prices)
+    kms_norm = normalize(kms)
+    prices_norm = normalize(prices)
     t0 = 0.0
     t1 = 0.0
     for _ in range(iterations):
-        grad0, grad1 = train_once(learning_rate, prices_norm, kms_norm, t0, t1)
+        grad0, grad1 = compute_gradients(learning_rate, prices_norm, kms_norm, t0, t1)
         t0 -= grad0
         t1 -= grad1
     return denormalize_thetas(t0, t1, km_min, km_max, price_min, price_max)