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numpy.core.maximum.reduce

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```reduce(a, axis=0, dtype=None, out=None, keepdims=False)

Reduces `a`'s dimension by one, by applying ufunc along one axis.

Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then
:math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
For a one-dimensional array, reduce produces results equivalent to:
::(more...)
```

```    m = u.shape[0]
n = vt.shape[1]
cutoff = rcond*maximum.reduce(s)
for i in range(min(n, m)):
if s[i] > cutoff:
```

```    m = u.shape[0]
n = vt.shape[1]
cutoff = rcond*maximum.reduce(s)
for i in range(min(n, m)):
if s[i] > cutoff:
```

```    m = u.shape[0]
n = vt.shape[1]
cutoff = rcond*maximum.reduce(s)
for i in range(min(n, m)):
if s[i] > cutoff:
```

```    m = u.shape[0]
n = vt.shape[1]
cutoff = rcond*maximum.reduce(s)
for i in range(min(n, m)):
if s[i] > cutoff:
```

```    m = u.shape[0]