I am using a custom metric function with scipy's cdist function. The custom function is something like
def cust_metric(u,v):
dist = np.cumsum(np.gcd(u,v) * k)
return dist
where k is an arbitrary coefficient.
Ideally, I was hoping to pass k as an argument when calling cdist like so:
d_ar = scipy.spatial.distance.cdist(arr1, arr2, metric=cust_metric(k=7))
However, this throws an error.
I was wondering if there is a simple solution that I may be missing? A quick but non-elegant fix is to declare k as a global variable and adjust it when needed.
According to its documentation, the value for metric should be a callable (or a string for a particular fixed collection). In your case you could obtain that through
def cust_metric(k):
return lambda u, v: np.cumsum(np.gcd(u, v) * k)
I do imagine your actual callable would look somewhat different since the moment u and v are 2D arrays, the np.cumsum returns an array, while the callable is supposed to produce a scalar. For example:
In [25]: arr1 = np.array([[5, 7], [6, 1]])
In [26]: arr2 = np.array([[6, 7], [6, 1]])
In [28]: def cust_metric(k):
...: return lambda u, v: np.sqrt(np.sum((k*u - v)**2))
...:
In [29]: scipy.spatial.distance.cdist(arr1, arr2, metric=cust_metric(k=7))
Out[29]:
array([[51.03920062, 56.08029957],
[36. , 36.49657518]])
In [30]: scipy.spatial.distance.cdist(arr1, arr2, metric=cust_metric(k=1))
Out[30]:
array([[1. , 6.08276253],
[6. , 0. ]])