cliassist

A command line client that assists user with auto-prompt of code/commands on CLI, based on text based queries using human-understandable language.

Requirement

Python 3 must be installed on your device.

To install other requirements. Execute :

python3 -m pip install requirements.txt

Usage

A command line client to accept user queries with the following command :

python3 CliAssist.py --query

Example:
        > python3 CliAssist.py --query
        Enter your query to search: How do I get a mean with numpy???

Json Output Generated

This is the example JSON that we generate out of the program in order to assist the user based on the text based queries obtained from the user.

Parameters:

a : array_like Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted. axis : None or int or tuple of ints, optional Axis or axes along which the means are computed. The default is to compute the mean of the flattened array. .. versionadded:: 1.7.0 If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before. dtype : data-type, optional Type to use in computing the mean. For integer inputs, the default is float64; for floating point inputs, it is the same as the input dtype. out : ndarray, optional Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the mean method of sub-classes of ndarray, however any non-default value will be. If the sub-class' method does not implement keepdims any exceptions will be raised.

Returns:

m : ndarray, see dtype parameter above If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned.

See Also:

average : Weighted average std, var, nanmean, nanstd, nanvar

Notes:

The arithmetic mean is the sum of the elements along the axis divided by the number of elements. Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue. By default, float16 results are computed using float32 intermediates for extra precision.

Examples:

>>> a = np.array([[1, 2], [3, 4]])

>>> np.mean(a)

2.5

>>> np.mean(a, axis=0)

array([2., 3.])

>>> np.mean(a, axis=1)

array([1.5, 3.5])

In single precision, mean can be inaccurate:

>>> a = np.zeros((2, 512*512), dtype=np.float32)

>>> a[0, :] = 1.0

>>> a[1, :] = 0.1

>>> np.mean(a)

0.54999924

Computing the mean in float64 is more accurate:

>>> np.mean(a, dtype=np.float64)

0.55000000074505806 # may vary

SAMPLES:

Project created by Diksha Kewat