version 0.0.1

pina/span.py

@@ -0,0 +1,68 @@
+import numpy as np
+from .chebyshev import chebyshev_roots
+import torch
+
+from .location import Location
+from .label_tensor import LabelTensor
+
+
+class Span(Location):
+    def __init__(self, span_dict):
+
+        self.fixed_ = {}
+        self.range_ = {}
+
+        for k, v in span_dict.items():
+            if isinstance(v, (int, float)):
+                self.fixed_[k] = v
+            elif isinstance(v, (list, tuple)) and len(v) == 2:
+                self.range_[k] = v
+            else:
+                raise TypeError
+
+    def sample(self, n, mode='random'):
+
+        bounds = np.array(list(self.range_.values()))
+        if mode == 'random':
+            pts = np.random.uniform(size=(n, bounds.shape[0]))
+        elif mode == 'chebyshev':
+            pts = np.array([
+                chebyshev_roots(n) * .5 + .5
+                for _ in range(bounds.shape[0])])
+            grids = np.meshgrid(*pts)
+            pts = np.hstack([grid.reshape(-1, 1) for grid in grids])
+        elif mode == 'grid':
+            pts = np.array([
+                np.linspace(0, 1, n)
+                for _ in range(bounds.shape[0])])
+            grids = np.meshgrid(*pts)
+            pts = np.hstack([grid.reshape(-1, 1) for grid in grids])
+        elif mode == 'lh' or mode == 'latin':
+            from scipy.stats import qmc
+            sampler = qmc.LatinHypercube(d=bounds.shape[0])
+            pts = sampler.random(n)
+
+        # Scale pts
+        pts *= bounds[:, 1] - bounds[:, 0]
+        pts += bounds[:, 0]
+        pts = torch.from_numpy(pts)
+        pts_range_ = LabelTensor(pts, list(self.range_.keys()))
+
+        fixed = torch.Tensor(list(self.fixed_.values()))
+        pts_fixed_ = torch.ones(pts_range_.tensor.shape[0], len(self.fixed_)) * fixed
+        pts_fixed_ = LabelTensor(pts_fixed_, list(self.fixed_.keys()))
+
+        if self.fixed_:
+            return LabelTensor.hstack([pts_range_, pts_fixed_])
+        else:
+            return pts_range_
+
+    def meshgrid(self, n):
+        pts = np.array([
+            np.linspace(0, 1, n)
+            for _ in range(self.bound.shape[0])])
+
+        pts *= self.bound[:, 1] - self.bound[:, 0]
+        pts += self.bound[:, 0]
+
+        return np.meshgrid(*pts)