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# `skopt`.dummy_minimize

skopt.dummy_minimize(func, dimensions, n_calls=100, initial_point_generator='random', x0=None, y0=None, random_state=None, verbose=False, callback=None, model_queue_size=None, init_point_gen_kwargs=None, space_constraint=None)

  Random search by uniform sampling within the given bounds.

  Parameters:
    func callable

      Function to minimize. Should take a single list of parameters and return the objective value.

      If you have a search-space where all dimensions have names, then you can use `skopt.utils.use_named_args()` as a decorator on your objective function, in order to call it directly with the named arguments. See `use_named_args` for an example.

    dimensions list, shape (n_dims,)

      List of search space dimensions. Each search dimension can be defined either as

      - a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer` dimensions),

      - a `(lower_bound, upper_bound, prior)` tuple (for `Real` dimensions),

      - as a list of categories (for `Categorical` dimensions), or

      - an instance of a `Dimension` object (`Real`, `Integer` or `Categorical`).

    n_calls int, default: 100

      Number of calls to `func` to find the minimum.

    initial_point_generator str, InitialPointGenerator instance, default: `"random"`

      Sets a initial points generator. Can be either

      - `"random"` for uniform random numbers,

      - `"sobol"` for a Sobol’ sequence,

      - `"halton"` for a Halton sequence,

      - `"hammersly"` for a Hammersly sequence,

      - `"lhs"` for a latin hypercube sequence,

      - `"grid"` for a uniform grid sequence

    x0 list, list of lists or `None`

      Initial input points.

      - If it is a list of lists, use it as a list of input points.

      - If it is a list, use it as a single initial input point.

      - If it is `None`, no initial input points are used.

    y0 list, scalar or `None`

      Evaluation of initial input points.

      - If it is a list, then it corresponds to evaluations of the function at each element of `x0` : the i-th element of `y0` corresponds to the function evaluated at the i-th element of `x0`.

      - If it is a scalar, then it corresponds to the evaluation of the function at `x0`.

      - If it is None and `x0` is provided, then the function is evaluated at each element of `x0`.

    random_state int, RandomState instance, or None (default)

      Set random state to something other than None for reproducible results.

    verbose boolean, default: False

      Control the verbosity. It is advised to set the verbosity to True for long optimization runs.

    callback callable, list of callables, optional

      If callable then `callback(res)` is called after each call to `func`. If list of callables, then each callable in the list is called.

    model_queue_size int or None, default: None

      Keeps list of models only as long as the argument given. In the case of None, the list has no capped length.

    space_constraint callable or None, default: None

      Constraint function. Should take a single list of parameters (i.e. a point in space) and return True if the point satisfies the constraints. If None, the space is not conditionally constrained.

  Returns:
    res `OptimizeResult`, scipy object

      The optimization result returned as a OptimizeResult object. Important attributes are:

      - `x` [list]: location of the minimum.

      - `fun` [float]: function value at the minimum.

      - `x_iters` [list of lists]: location of function evaluation for each iteration.

      - `func_vals` [array]: function value for each iteration.

      - `space` [Space]: the optimisation space.

      - `specs` [dict]: the call specifications.

      - `rng` [RandomState instance]: State of the random state at the end of minimization.

      For more details related to the OptimizeResult object, refer http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html

    See also

    functions `skopt.gp_minimize`, `skopt.forest_minimize`, `skopt.gbrt_minimize`