CNN v2: Change feature #6 from sin(10*x) to sin(20*y)

Update positional encoding to use vertical coordinate at higher frequency.

Changes:
- train_cnn_v2.py: sin10_x → sin20_y (computed from uv_y)
- cnn_v2_static.wgsl: sin10_x → sin20_y (computed from uv_y)
- index.html: sin10_x → sin20_y (STATIC_SHADER)
- CNN_V2.md: Update feature descriptions and examples
- CNN_V2_BINARY_FORMAT.md: Update static features documentation

Feature vector: [p0, p1, p2, p3, uv_x, uv_y, sin20_y, bias]

Rationale: Higher frequency (20 vs 10) + vertical axis provides better
spatial discrimination for position encoding.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

1 files changed, 6 insertions, 6 deletions

diff --git a/training/train_cnn_v2.py b/training/train_cnn_v2.py
index 1487c08..a9a311a 100755
--- a/training/train_cnn_v2.py
+++ b/training/train_cnn_v2.py
@@ -30,13 +30,13 @@ def compute_static_features(rgb, depth=None, mip_level=0):
         mip_level: Mip level for p0-p3 (0=original, 1=half, 2=quarter, 3=eighth)
 
     Returns:
-        (H, W, 8) static features: [p0, p1, p2, p3, uv_x, uv_y, sin10_x, bias]
+        (H, W, 8) static features: [p0, p1, p2, p3, uv_x, uv_y, sin20_y, bias]
 
     Note: p0-p3 are parametric features generated from specified mip level
 
     TODO: Binary format should support arbitrary layout and ordering for feature vector (7D),
           alongside mip-level indication. Current layout is hardcoded as:
-          [p0, p1, p2, p3, uv_x, uv_y, sin10_x, bias]
+          [p0, p1, p2, p3, uv_x, uv_y, sin20_y, bias]
           Future: Allow experimentation with different feature combinations without shader recompilation.
           Examples: [R, G, B, dx, dy, uv_x, bias] or [mip1.r, mip2.g, laplacian, uv_x, sin20_x, bias]
     """
@@ -68,13 +68,13 @@ def compute_static_features(rgb, depth=None, mip_level=0):
     uv_y = np.linspace(0, 1, h)[:, None].repeat(w, axis=1).astype(np.float32)
 
     # Multi-frequency position encoding
-    sin10_x = np.sin(10.0 * uv_x).astype(np.float32)
+    sin20_y = np.sin(20.0 * uv_y).astype(np.float32)
 
     # Bias dimension (always 1.0) - replaces Conv2d bias parameter
     bias = np.ones((h, w), dtype=np.float32)
 
-    # Stack: [p0, p1, p2, p3, uv.x, uv.y, sin10_x, bias]
-    features = np.stack([p0, p1, p2, p3, uv_x, uv_y, sin10_x, bias], axis=-1)
+    # Stack: [p0, p1, p2, p3, uv.x, uv.y, sin20_y, bias]
+    features = np.stack([p0, p1, p2, p3, uv_x, uv_y, sin20_y, bias], axis=-1)
     return features
 
 
@@ -376,7 +376,7 @@ def train(args):
                     'kernel_sizes': kernel_sizes,
                     'num_layers': args.num_layers,
                     'mip_level': args.mip_level,
-                    'features': ['p0', 'p1', 'p2', 'p3', 'uv.x', 'uv.y', 'sin10_x', 'bias']
+                    'features': ['p0', 'p1', 'p2', 'p3', 'uv.x', 'uv.y', 'sin20_y', 'bias']
                 }
             }, checkpoint_path)
             print(f"  → Saved checkpoint: {checkpoint_path}")