.. currentmodule:: skopt

===============
Getting started
===============
Scikit-Optimize, or ``skopt``, is a simple and efficient library to
minimize (very) expensive and noisy black-box functions. It implements
several methods for sequential model-based optimization. ``skopt`` aims
to be accessible and easy to use in many contexts.

The library is built on top of NumPy, SciPy and Scikit-Learn.

We do not perform gradient-based optimization. For gradient-based
optimization algorithms look at
``scipy.optimize``
`here <http://docs.scipy.org/doc/scipy/reference/optimize.html>`_.

.. figure:: https://rawgit.com/scikit-optimize/scikit-optimize/master/media/bo-objective.png
   :alt: Approximated objective

Approximated objective function after 50 iterations of :class:`gp_minimize`.
Plot made using :class:`plots.plot_objective`.

Finding a minimum
=================

Find the minimum of the noisy function ``f(x)`` over the range ``-2 < x < 2``
with :class:`skopt`::

  import numpy as np
  from skopt import gp_minimize

  def f(x):
      return (np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) *
              np.random.randn() * 0.1)

  res = gp_minimize(f, [(-2.0, 2.0)])

For more control over the optimization loop you can use the :class:`skopt.Optimizer`
class::

  from skopt import Optimizer

  opt = Optimizer([(-2.0, 2.0)])

  for i in range(20):
      suggested = opt.ask()
      y = f(suggested)
      opt.tell(suggested, y)
      print('iteration:', i, suggested, y)

For more read our :ref:`sphx_glr_auto_examples_bayesian-optimization.py` and the other
`examples <auto_examples/index.html>`_.