From b1c0a419c06205fc68c05007b0a796ffc0c4d7a0 Mon Sep 17 00:00:00 2001
From: Nando Farchmin <nando.farchmin@gmail.com>
Date: Wed, 11 May 2022 18:38:13 +0200
Subject: [PATCH] Add basic package setup
---
app/function_approximation.py | 21 ++++++++++++++++
src/__init__.py | 0
src/misc.py | 45 +++++++++++++++++++++++++++++++++++
src/target_function.py | 23 ++++++++++++++++++
4 files changed, 89 insertions(+)
create mode 100644 app/function_approximation.py
create mode 100644 src/__init__.py
create mode 100644 src/misc.py
create mode 100644 src/target_function.py
diff --git a/app/function_approximation.py b/app/function_approximation.py
new file mode 100644
index 0000000..d2cd790
--- /dev/null
+++ b/app/function_approximation.py
@@ -0,0 +1,21 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+from src.misc import time_stamp, timeit
+from src import target_function
+
+
+def main() -> None:
+ print(time_stamp(), "Initialize main file")
+ with timeit("create x_train data ({:4.2f} s)"):
+ x_train = np.random.uniform(0, 1, (100000, 2))
+ with timeit("create y_train data ({:4.2f} s)"):
+ y_train = target_function.sin2d(x_train)
+
+ plt.figure()
+ plt.hexbin(x_train[:, 0], x_train[:, 1], y_train, gridsize=50)
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/misc.py b/src/misc.py
new file mode 100644
index 0000000..5e1f8a2
--- /dev/null
+++ b/src/misc.py
@@ -0,0 +1,45 @@
+from typing import List, Union
+import time
+from contextlib import contextmanager
+import numpy as np
+
+
+def time_stamp():
+ """Time stamp for logging."""
+ return time.strftime("[%Y-%m-%d %H:%M:%S]", time.localtime())
+
+
+@contextmanager
+def timeit(title: str, verbose: bool = True) -> None:
+ """Time functions and print time."""
+ start = time.perf_counter()
+ try:
+ yield
+ finally:
+ if verbose:
+ print(time_stamp(), title.format(time.perf_counter()-start))
+
+
+def cart_prod(array_list: Union[List[np.ndarray], np.ndarray]) -> np.ndarray:
+ """Compute the outer product of two or more arrays.
+
+ Assemble an array containing all possible combinations of the elements
+ of the input vectors :math:`v_1,\\dots,v_n`.
+
+ Parameters
+ ----------
+ array_list : list of array_like
+ List of vectors :math:`v_1,\\dots,v_n`.
+
+ Returns
+ -------
+ :
+ Cartesian product array.
+ """
+ dim = len(array_list)
+ if dim == 1:
+ return np.array(array_list).T
+ x = np.hstack((np.meshgrid(*array_list))).swapaxes(0, 1).reshape(dim, -1).T
+ return x
+
+
diff --git a/src/target_function.py b/src/target_function.py
new file mode 100644
index 0000000..37c9851
--- /dev/null
+++ b/src/target_function.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+
+def sin2d(xs: np.ndarray) -> np.ndarray:
+ """Benchmark function for approximation.
+
+ The benchmark function is given by
+
+ .. math::
+ f(x_1, x_2) = \\sin(4\\pi x_1) \\sin(3\\pi x_2)
+
+ Parameters
+ ----------
+ xs : np.ndarray
+ Evaluation points. Shape is (n, 2).
+
+ Returns
+ -------
+ :
+ Function evaluations.
+ """
+ assert xs.ndim == 2 and xs.shape[1] == 2
+ return np.sin(4*np.pi*xs[:, 0]) * np.sin(3*np.pi*xs[:, 1])
--
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