zoeygxy commented on a change in pull request #15390: [Numpy fix-doc]modify
numpy doc
URL: https://github.com/apache/incubator-mxnet/pull/15390#discussion_r299303415
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File path: python/mxnet/symbol/numpy/_symbol.py
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@@ -1555,4 +1613,156 @@ def sqrt(x, out=None, **kwargs):
return _unary_func_helper(x, _npi.sqrt, _np.sqrt, out=out, **kwargs)
+@set_module('mxnet.symbol.numpy')
+def ceil(x, out=None, **kwargs):
+ r"""
+ Return the ceiling of the input, element-wise.
+
+ The ceil of the ndarray `x` is the smallest integer `i`, such that
+ `i >= x`. It is often denoted as :math:`\lceil x \rceil`.
+
+ Parameters
+ ----------
+ x : _Symbol or scalar
+ Input array.
+ out : _Symbol or None
+ A location into which the result is stored. If provided, it
+ must have a shape that the inputs broadcast to. If not provided
+ or None, a freshly-allocated array is returned. The dtype of the
+ output is the same as that of the input if the input is an ndarray.
+
+ Returns
+ -------
+ y :
+ _Symbol or scalar
+ The ceiling of each element in `x`, with `float` dtype.
+ This is a scalar if `x` is a scalar.
+
+ Examples
+ --------
+ >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
+ >>> np.ceil(a)
+ array([-1., -1., -0., 1., 2., 2., 2.])
+
+ >>> #if you use parameter out, x and out must be ndarray. if not, you will
get an error!
+ >>> a = np.array(1)
+ >>> np.ceil(np.array(3.5), a)
+ array(4.)
+ >>> a
+ array(4.)
+
+ """
+ return _unary_func_helper(x, _npi.ceil, _np.ceil, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def log1p(x, out=None, **kwargs):
+ """
+ Return the natural logarithm of one plus the input array, element-wise.
+
+ Calculates ``log(1 + x)``.
+
+ Parameters
+ ----------
+ x :
+ _Symbol or scalar
+ Input array.
+ out : _Symbol or None
+ A location into which the result is stored. If provided, it
+ must have a shape that the inputs broadcast to. If not provided
+ or None, a freshly-allocated array is returned. The dtype of the
+ output is the same as that of the input if the input is an ndarray.
+
+ Returns
+ -------
+ y : _Symbol or scalar
+ Natural logarithm of 1 + x, element-wise. This is a scalar
+ if x is a scalar.
+
+ Notes
+ -----
+
+ For real-valued input, `log1p` is accurate also for `x` so small
+ that `1 + x == 1` in floating-point accuracy.
+
+ Logarithm is a multivalued function: for each `x` there is an infinite
+ number of `z` such that `exp(z) = 1 + x`. The convention is to return
+ the `z` whose imaginary part lies in `[-pi, pi]`.
+
+ For real-valued input data types, `log1p` always returns real output.
+ For each value that cannot be expressed as a real number or infinity,
+ it yields ``nan`` and sets the `invalid` floating point error flag.
+
+ For complex-valued input, `log1p` is a complex analytical function that
+ has a branch cut `[-inf, -1]` and is continuous from above on it.
+ `log1p` handles the floating-point negative zero as an infinitesimal
+ negative number, conforming to the C99 standard.
+
+ Examples
+ --------
+ >>> np.log1p(1e-99)
+ 1e-99
+
+ """
+ return _unary_func_helper(x, _npi.log1p, _np.log1p, out=out, **kwargs)
+
+
+@set_module('mxnet.symbol.numpy')
+def tanh(x, out=None, **kwargs):
+ """
+ Compute hyperbolic tangent element-wise.
+
+ Equivalent to ``np.sinh(x)/np.cosh(x)``.
+
+ Parameters
+ ----------
+ x :
+ _Symbol
+ Input array.
+ out : _Symbol or None
+ A location into which the result is stored. If provided, it
+ must have a shape that the inputs broadcast to. If not provided
+ or None, a freshly-allocated array is returned. The dtype of the
+ output is the same as that of the input if the input is an ndarray.
+ Returns
+ -------
+ y : _Symbol
+ The corresponding hyperbolic tangent values.
+
+ Notes
+ -----
+ If `out` is provided, the function writes the result into it,
+ and returns a reference to `out`. (See Examples)
+
+ - Not support complex computation (like imaginary number)
+
+ >>> np.tanh(np.pi*1j)
+ TypeError: type <type 'complex'> not supported
+
+ Examples
+ --------
+ >>> np.tanh(np.array[0, np.pi]))
Review comment:
@gyshi You have omitted one left parenthesis here
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