One-hot multinomial logisitc regression
Project description
One-hot multinomial logistic regression
========================
Quick Start
-----------
Installation
~~~~~~~~~~~~
- To install ``ohmlr`` on your computer using ``pip``, execute
.. code-block:: sh
pip install ohmlr
- Test out ``ohmlr`` in Python:
.. code-block:: python
import ohmlr
import numpy as np
# create model and generate data
n_features = 16
n_x_classes = np.random.randint(2, 10, size=n_features)
n_y_classes = 8
model = ohmlr.ohmlr().random(n_features, n_x_classes, n_y_classes)
x, y = model.generate_data(n_samples=1000)
# fit and score model
model.fit(x, y)
print(model.score(x, y))
Links
-----
Online documentation:
http://joepatmckenna.github.io/ohmlr
Source code repository:
https://github.com/joepatmckenna/ohmlr
Python package index:
https://pypi.python.org/pypi/ohmlr
========================
Quick Start
-----------
Installation
~~~~~~~~~~~~
- To install ``ohmlr`` on your computer using ``pip``, execute
.. code-block:: sh
pip install ohmlr
- Test out ``ohmlr`` in Python:
.. code-block:: python
import ohmlr
import numpy as np
# create model and generate data
n_features = 16
n_x_classes = np.random.randint(2, 10, size=n_features)
n_y_classes = 8
model = ohmlr.ohmlr().random(n_features, n_x_classes, n_y_classes)
x, y = model.generate_data(n_samples=1000)
# fit and score model
model.fit(x, y)
print(model.score(x, y))
Links
-----
Online documentation:
http://joepatmckenna.github.io/ohmlr
Source code repository:
https://github.com/joepatmckenna/ohmlr
Python package index:
https://pypi.python.org/pypi/ohmlr
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