Merge pull request #17 from formbay/min_points_error

Fixed error when n of points is less than self.min_points

fastpair/base.py

@@ -45,6 +45,7 @@
 
 class attrdict(dict):
     """Simple dict with support for accessing elements as attributes."""
+
     def __init__(self, *args, **kwargs):
         super(attrdict, self).__init__(*args, **kwargs)
         self.__dict__ = self
@@ -53,6 +54,7 @@ def __init__(self, *args, **kwargs):
 class FastPair(object):
     """FastPair 'sketch' class.
     """
+
     def __init__(self, min_points=10, dist=dist.euclidean):
         """Initialize an empty FastPair data-structure.
 
@@ -145,11 +147,11 @@ def build(self, points=None):
         np = len(self)
 
         # Go through and find all neighbors, placing then in a conga line
-        for i in range(np-1):
+        for i in range(np - 1):
             # Find neighbor to p[0] to start
             nbr = i + 1
             nbd = float("inf")
-            for j in range(i+1, np):
+            for j in range(i + 1, np):
                 d = self.dist(self.points[i], self.points[j])
                 if d < nbd:
                     nbr = j
@@ -161,8 +163,8 @@ def build(self, points=None):
             self.points[i + 1] = self.neighbors[self.points[i]].neigh
         # No more neighbors, terminate conga line.
         # Last person on the line has no neigbors :(
-        self.neighbors[self.points[np-1]].neigh = self.points[np-1]
-        self.neighbors[self.points[np-1]].dist = float("inf")
+        self.neighbors[self.points[np - 1]].neigh = self.points[np - 1]
+        self.neighbors[self.points[np - 1]].dist = float("inf")
         self.initialized = True
         return self
 
@@ -187,7 +189,7 @@ def closest_pair(self):
 
     def closest_pair_brute_force(self):
         """Find closest pair using brute-force algorithm."""
-        return _closest_pair_brute_force(self.points)
+        return _closest_pair_brute_force(self.points, self.dist)
 
     def sdist(self, p):
         """Compute distances from input to all other points in data-structure.
@@ -204,8 +206,7 @@ def sdist(self, p):
         >>> fp = FastPair().build(points)
         >>> min(fp.sdist(point), key=itemgetter(0))
         """
-        return ((self.dist(a, b), b) for a, b in
-                zip(cycle([p]), self.points) if b != a)
+        return ((self.dist(a, b), b) for a, b in zip(cycle([p]), self.points) if b != a)
 
     def _find_neighbor(self, p):
         """Find and update nearest neighbor of a given point."""
@@ -221,7 +222,7 @@ def _find_neighbor(self, p):
             self.neighbors[p].neigh = self.points[first_nbr]
             self.neighbors[p].dist = self.dist(p, self.neighbors[p].neigh)
             # Now test whether each other point is closer
-            for q in self.points[first_nbr+1:]:
+            for q in self.points[first_nbr + 1 :]:
                 if p != q:
                     d = self.dist(p, q)
                     if d < self.neighbors[p].dist:

fastpair/test/test_fastpair.py

@@ -21,6 +21,38 @@
 from math import isinf, isnan
 
 from scipy import mean, array, unique
+import numpy as np
+
+
+def normalized_distance(_a, _b):
+    b = _b.astype(int)
+    a = _a.astype(int)
+    norm_diff = np.linalg.norm(b - a)
+    norm1 = np.linalg.norm(b)
+    norm2 = np.linalg.norm(a)
+    return norm_diff / (norm1 + norm2)
+
+
+def image_distance(image1, image2):
+    (sig1, _) = image1
+    (sig2, _) = image2
+    sig1 = np.frombuffer(sig1, np.int8)
+    sig2 = np.frombuffer(sig2, np.int8)
+    return normalized_distance(sig1, sig2)
+
+
+# Setup fixtures
+@pytest.fixture(scope="module")
+def image_array():
+    return [
+        (b"\x00\x00\x07\x20\x00\x00\x03\x21\x08\x02\x00\x00\x00", "0"),
+        (b"\x00\x50\x07\x60\x00\x00\x03\x21\x06\x02\x00\x00\x00", "1"),
+        (b"\x00\x00\x07\x20\x00\x00\x03\x21\x08\x02\x00\x08\x00", "2"),
+        (b"\x00\x50\x07\x60\x00\x00\x03\x21\x06\x02\x00\x60\x00", "3"),
+        (b"\x00\x00\x07\x20\x00\x00\x03\x21\x08\x02\x00\x30\x01", "4"),
+        (b"\x00\x50\x07\x60\x00\x00\x03\x21\x06\x02\x00\x00\x10", "5"),
+    ]
+
 
 def contains_same(s, t):
     s, t = set(s), set(t)
@@ -29,9 +61,11 @@ def contains_same(s, t):
 
 def all_close(s, t, tol=1e-8):
     # Ignores inf and nan values...
-    return all(abs(a - b) < tol for a, b in zip(s, t)
-               if not isinf(a) and not isinf(b) and
-                  not isnan(a) and not isnan(b))
+    return all(
+        abs(a - b) < tol
+        for a, b in zip(s, t)
+        if not isinf(a) and not isinf(b) and not isnan(a) and not isnan(b)
+    )
 
 
 def rand_tuple(dim=2):
@@ -89,7 +123,7 @@ def test_sub(self, PointSet):
         assert end["neigh"] != ps[-1]
         # This is risky, because it might legitimately be the same...?
         assert start["dist"] != end["dist"]
-        assert len(fp) == len(ps)-1
+        assert len(fp) == len(ps) - 1
         with pytest.raises(ValueError):
             fp -= rand_tuple(len(ps[0]))
 
@@ -124,7 +158,10 @@ def test_all_closest_pairs(self, PointSet):
         # dc = fp.closest_pair_divide_conquer()  # Maybe different ordering
         assert abs(cp[0] - bf[0]) < 1e-8
         assert cp[1] == bf[1]  # Tuple comparison
-        test = min([(fp.dist(a, b), (a, b)) for a, b in combinations(ps, r=2)], key=itemgetter(0))
+        test = min(
+            [(fp.dist(a, b), (a, b)) for a, b in combinations(ps, r=2)],
+            key=itemgetter(0),
+        )
         assert abs(cp[0] - test[0]) < 1e-8
         assert sorted(cp[1]) == sorted(test[1])  # Tuple comparison
         # assert abs(dc[0] - cp[0]) < 1e-8  # Compare distance
@@ -137,7 +174,7 @@ def test_find_neighbor_and_sdist(self, PointSet):
         rando = rand_tuple(len(ps[0]))
         neigh = fp._find_neighbor(rando)  # Abusing find_neighbor!
         dist = fp.dist(rando, neigh["neigh"])
-        assert  abs(dist - neigh["dist"]) < 1e-8
+        assert abs(dist - neigh["dist"]) < 1e-8
         assert len(fp) == len(ps)  # Make sure we didn't add a point...
         l = [(fp.dist(a, b), b) for a, b in zip(cycle([rando]), ps)]
         res = min(l, key=itemgetter(0))
@@ -150,16 +187,18 @@ def test_find_neighbor_and_sdist(self, PointSet):
     def test_cluster(self, PointSet):
         ps = PointSet
         fp = FastPair().build(ps)
-        for i in range(len(fp)-1):
+        for i in range(len(fp) - 1):
             # Version one
             dist, (a, b) = fp.closest_pair()
             c = interact(a, b)
             fp -= b  # Drop b
             fp -= a
             fp += c
             # Order gets reversed here...
-            d, (e, f) = min([(fp.dist(i, j), (i, j)) for i, j in
-                             combinations(ps, r=2)], key=itemgetter(0))
+            d, (e, f) = min(
+                [(fp.dist(i, j), (i, j)) for i, j in combinations(ps, r=2)],
+                key=itemgetter(0),
+            )
             g = interact(e, f)
             assert abs(d - dist) < 1e-8
             assert (a == e or b == e) and (b == f or a == f)
@@ -184,8 +223,8 @@ def test_update_point(self, PointSet):
         l = [(fp.dist(a, b), b) for a, b in zip(cycle([new]), ps)]
         res = min(l, key=itemgetter(0))
         neigh = fp.neighbors[new]
-        #assert abs(res[0] - neigh["dist"]) < 1e-8
-        #assert res[1] == neigh["neigh"]
+        # assert abs(res[0] - neigh["dist"]) < 1e-8
+        # assert res[1] == neigh["neigh"]
 
     def test_merge_closest(self):
         # This needs to be 'fleshed' out more... lots of things to test here
@@ -201,10 +240,60 @@ def test_merge_closest(self):
             fp._update_point(a, new)
             n -= 1
         assert len(fp) == 1 == n
-        points = [(0.69903599809571437, 0.52457534006594131,
-                   0.7614753848101149, 0.37011695654655385)]
+        points = [
+            (
+                0.69903599809571437,
+                0.52457534006594131,
+                0.7614753848101149,
+                0.37011695654655385,
+            )
+        ]
         assert all_close(fp.points[0], points[0])
         # Should have < 2 points now...
         with pytest.raises(ValueError):
             fp.closest_pair()
             # fp2.closest_pair()
+
+    def test_call_and_closest_pair_min_points(self, image_array):
+        ps = image_array
+        fp = FastPair(dist=image_distance)
+        for p in ps:
+            fp += p
+        assert fp.initialized is False
+        assert len(fp) == 6
+        cp = fp.closest_pair()
+        bf = fp.closest_pair_brute_force()
+        assert fp() == cp
+        assert abs(cp[0] - bf[0]) < 1e-8
+        assert cp[1] == bf[1]
+
+    def test_iter(self, PointSet):
+        ps = PointSet
+        fp = FastPair().build(ps)
+        assert fp.min_points == 10
+        assert isinstance(fp.dist, FunctionType)
+        my_iter = iter(fp)
+        assert next(my_iter) in set(ps)
+        assert fp[ps[0]].neigh in set(ps)
+
+        try:
+            myitem = fp[(2, 3, 4)]
+        except KeyError as err:
+            print(err)
+
+        fp[ps[0]] = fp[ps[0]].neigh
+        try:
+            fp[(2, 3, 4)] = fp[ps[0]].neigh
+        except KeyError as err:
+            print(err)
+
+    def test_update_point_less_points(self, PointSet):
+        ps = PointSet
+        fp = FastPair()
+        for p in ps[:9]:
+            fp += p
+        assert fp.initialized is False
+        old = ps[0]  # Just grab the first point...
+        new = rand_tuple(len(ps[0]))
+        res = fp._update_point(old, new)
+        assert len(fp) == 1