We no longer support numpy < 1.7

Author: lmcinnes

hdbscan/hdbscan_.py

@@ -43,55 +43,7 @@
 #         John Healy <[email protected]>
 #
 # License: BSD 3 clause
-
-# Supporting numpy prior to version 1.7 is a little painful ...
-if hasattr(np, 'isclose'):
-    from numpy import isclose
-else:
-    def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
-
-        def within_tol(x, y, atol, rtol):
-            with np.errstate(invalid='ignore'):
-                result = np.less_equal(abs(x - y), atol + rtol * abs(y))
-            if np.isscalar(a) and np.isscalar(b):
-                result = bool(result)
-            return result
-
-        x = np.array(a, copy=False, subok=True, ndmin=1)
-        y = np.array(b, copy=False, subok=True, ndmin=1)
-
-        # Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT).
-        # This will cause casting of x later. Also, make sure to allow
-        # subclasses (e.g., for numpy.ma).
-        dt = np.core.multiarray.result_type(y, 1.)
-        y = np.array(y, dtype=dt, copy=False, subok=True)
-
-        xfin = np.isfinite(x)
-        yfin = np.isfinite(y)
-        if np.all(xfin) and np.all(yfin):
-            return within_tol(x, y, atol, rtol)
-        else:
-            finite = xfin & yfin
-            cond = np.zeros_like(finite, subok=True)
-            # Because we're using boolean indexing, x & y must be the same
-            # shape. Ideally, we'd just do x, y = broadcast_arrays(x, y).
-            # It's in lib.stride_tricks, though, so we can't import it here.
-            x = x * np.ones_like(cond)
-            y = y * np.ones_like(cond)
-            # Avoid subtraction with infinite/nan values...
-            cond[finite] = within_tol(x[finite], y[finite], atol, rtol)
-            # Check for equality of infinite values...
-            cond[~finite] = (x[~finite] == y[~finite])
-            if equal_nan:
-                # Make NaN == NaN
-                both_nan = np.isnan(x) & np.isnan(y)
-                cond[both_nan] = both_nan[both_nan]
-
-            if np.isscalar(a) and np.isscalar(b):
-                return bool(cond)
-            else:
-                return cond
-
+from numpy import isclose
 
 def _tree_to_labels(X, single_linkage_tree, min_cluster_size=10,
                     cluster_selection_method='eom',