From 2e1e002d3c35df97ec30df2244f2fae271eec29d Mon Sep 17 00:00:00 2001
From: Steve Schmerler <git@elcorto.com>
Date: Thu, 8 May 2025 12:28:26 +0200
Subject: [PATCH] gp: 01_one_dim: plot ground truth
---
BLcourse2.3/01_one_dim.py | 33 +++++++++++++++++++++++++++++----
1 file changed, 29 insertions(+), 4 deletions(-)
diff --git a/BLcourse2.3/01_one_dim.py b/BLcourse2.3/01_one_dim.py
index f9f8f95..1b175af 100644
--- a/BLcourse2.3/01_one_dim.py
+++ b/BLcourse2.3/01_one_dim.py
@@ -77,26 +77,35 @@ torch.manual_seed(123)
# +
+def ground_truth(x, const):
+ return torch.sin(x) * torch.exp(-0.2 * x) + const
+
+
def generate_data(x, gaps=[[1, 3]], const=5):
- y = torch.sin(x) * torch.exp(-0.2 * x) + torch.randn(x.shape) * 0.1 + const
+ y = ground_truth(x, const=const) + torch.randn(x.shape) * 0.1
msk = torch.tensor([True] * len(x))
if gaps is not None:
for g in gaps:
msk = msk & ~((x > g[0]) & (x < g[1]))
- return x[msk], y[msk]
+ return x[msk], y[msk], y
+const = 5.0
x = torch.linspace(0, 4 * math.pi, 100)
-X_train, y_train = generate_data(x, gaps=[[6, 10]])
+X_train, y_train, y_gt_train = generate_data(x, gaps=[[6, 10]], const=const)
X_pred = torch.linspace(
X_train[0] - 2, X_train[-1] + 2, 200, requires_grad=False
)
+y_gt_pred = ground_truth(X_pred, const=const)
print(f"{X_train.shape=}")
print(f"{y_train.shape=}")
print(f"{X_pred.shape=}")
-plt.scatter(X_train, y_train, marker="o", color="tab:blue")
+fig, ax = plt.subplots()
+ax.scatter(X_train, y_train, marker="o", color="tab:blue", label="noisy data")
+ax.plot(X_pred, y_gt_pred, ls="--", color="k", label="ground truth")
+ax.legend()
# -
# # Define GP model
@@ -264,6 +273,14 @@ with torch.no_grad():
color="tab:red",
lw=2,
)
+ ax.plot(
+ X_pred.numpy(),
+ y_gt_pred.numpy(),
+ label="ground truth",
+ color="k",
+ lw=2,
+ ls="--",
+ )
ax.fill_between(
X_pred.numpy(),
lower.numpy(),
@@ -377,6 +394,14 @@ with torch.no_grad():
color="tab:red",
lw=2,
)
+ ax.plot(
+ X_pred.numpy(),
+ y_gt_pred.numpy(),
+ label="ground truth",
+ color="k",
+ lw=2,
+ ls="--",
+ )
ax.fill_between(
X_pred.numpy(),
lower.numpy(),
--
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