Pynomaly

Fixed
- Fixed an issue which allowed the number of neighbors considered to exceed the number of observations. Added a check
to ensure this is no longer possible.

Fixed
- Fixed an issue inadvertently introduced in 0.1.6 that caused distance calculations to be incorrect,
thus resulting in incorrect LoOP values.

Fixed
- Updated the distance calculation such that the euclidean distance calculation has been separated from
the main distance calculation function.
- Fixed an error in the calculation of the standard distance.

Changed
- .fit() now returns a fitted object instead of local_outlier_probabilities. Local outlier probabilities can
be now be retrieved by calling .local_outlier_probabilities. See the readme for an example.
- Some private functions have been renamed.

Fixed
- [Issue 4](https://github.com/vc1492a/PyNomaly/issues/4) - Separated parameter type checks
from checks for invalid parameter values.
- accepts decorator verifies LocalOutlierProbability parameters are of correct type.
- Parameter value checks moved from .fit() to init.
- Fixed parameter check to ensure extent value is in the range (0., 1.] instead of [0, 1] (extent cannot be zero).
- [Issue 1](https://github.com/vc1492a/PyNomaly/issues/1) - Added type check using accepts decorator for cluster_labels.

Fixed
- [Issue 3](https://github.com/vc1492a/PyNomaly/issues/3) - .fit() fails if the sum of squared distances sums to 0.
- Added check to ensure the sum of square distances is greater than zero.
- Added UserWarning to increase the neighborhood size if all neighbors in n_neighbors are
zero distance from an observation.
- Added UserWarning to check for integer type n_neighbor conditions versus float type.
- Changed calculation of the probabilistic local outlier factor expected value to Numpy operation
from base Python.

Fixed
- Altered the distance matrix computation to return a triangular matrix instead of a
fully populated matrix. This was made to ensure no duplicate neighbors were present
in computing the neighborhood distance for each observation.