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Bayesian optimization using Gaussian Processes.

If every function evaluation is expensive, for instancewhen the parameters are the hyperparameters of a neural networkand the function evaluation is the mean cross-validation score acrossten folds, optimizing the hyperparameters by standard optimizationroutines would take for ever!

The idea is to approximate the function using a Gaussian process.In other words the function values are assumed to follow a multivariategaussian. The covariance of the function values are given by aGP kernel between the parameters. Then a smart choice to choose thenext parameter to evaluate can be made by the acquisition functionover the Gaussian prior which is much quicker to evaluate.

The total number of evaluations, n_calls, are performed like thefollowing. If x0 is provided but not y0, then the elements of x0are first evaluated, followed by n_initial_points evaluations.Finally, n_calls-len(x0)-n_initial_points evaluations aremade guided by the surrogate model. If x0 and y0 are bothprovided then n_initial_points evaluations are first made thenn_calls-n_initial_points subsequent evaluations are madeguided by the surrogate model.

The first n_initial_points are generated by theinitial_point_generator.

Parameters

funccallable

Function to minimize. Should take a single list of parametersand 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 decoratoron your objective function, in order to call it directlywith 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 Integerdimensions),

• a (lower_bound,upper_bound,'prior') tuple (for Realdimensions),

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

• an instance of a Dimension object (Real, Integer orCategorical).

Note System lens 2 1.

The upper and lower bounds are inclusive for Integerdimensions.

base_estimatora Gaussian process estimator

The Gaussian process estimator to use for optimization.By default, a Matern kernel is used with the followinghyperparameters tuned.

• All the length scales of the Matern kernel.

• The covariance amplitude that each element is multiplied with.

• Noise that is added to the matern kernel. The noise is assumedto be iid gaussian.

n_callsint, default: 100

Number of calls to func.

n_random_startsint, default: None

Number of evaluations of func with random points beforeapproximating it with base_estimator.

Deprecated since version 0.8: use n_initial_points instead.

n_initial_pointsint, default: 10

Number of evaluations of func with initialization pointsbefore approximating it with base_estimator. Initial pointgenerator can be changed by setting initial_point_generator.

initial_point_generatorstr, 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,

acq_funcstring, default: 'gp_hedge'

Function to minimize over the gaussian prior. Can be either

• 'LCB' for lower confidence bound.

• 'EI' for negative expected improvement.

• 'PI' for negative probability of improvement.

• 'gp_hedge' Probabilistically choose one of the above threeacquisition functions at every iteration. The weightagegiven to these gains can be set by (eta) throughacq_func_kwargs.

  • The gains g_i are initialized to zero.

  • At every iteration,

    • Each acquisition function is optimised independently topropose an candidate point X_i.

    • Out of all these candidate points, the next point X_best ischosen by (softmax(eta g_i))

    • After fitting the surrogate model with (X_best,y_best),the gains are updated such that (g_i -= mu(X_i))

• 'EIps' for negated expected improvement per second to take intoaccount the function compute time. Then, the objective function isassumed to return two values, the first being the objective value andthe second being the time taken in seconds.

• 'PIps' for negated probability of improvement per second. Thereturn type of the objective function is assumed to be similar tothat of 'EIps'

acq_optimizerstring, 'sampling' or 'lbfgs', default: 'lbfgs'

Method to minimize the acquisition function. The fit modelis updated with the optimal value obtained by optimizing acq_funcwith acq_optimizer.

The acq_func is computed at n_points sampled randomly.

• If set to 'auto', then acq_optimizer is configured on thebasis of the space searched over.If the space is Categorical then this is set to be 'sampling'.

• If set to 'sampling', then the point among these n_pointswhere the acq_func is minimum is the next candidate minimum.

• If set to 'lbfgs', then

  • The n_restarts_optimizer no. of points which the acquisitionfunction is least are taken as start points.

  • 'lbfgs' is run for 20 iterations with these points as initialpoints to find local minima.

  • The optimal of these local minima is used to update the prior.

x0list, 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. Screenflick 2 7 32 – capture screen movement to videos.

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

y0list, scalar or None

Evaluation of initial input points.

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

• If it is a scalar, then it corresponds to the evaluation of thefunction at x0.

• Typeeto 1 2 download free. If it is None and x0 is provided, then the function is evaluatedat each element of x0.

random_stateint, RandomState instance, or None (default)

Set random state to something other than None for reproducibleresults.

verboseboolean, default: False

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

callbackcallable, 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.

n_pointsint, default: 10000

Number of points to sample to determine the next 'best' point.Useless if acq_optimizer is set to 'lbfgs'.

n_restarts_optimizerint, default: 5

The number of restarts of the optimizer when acq_optimizeris 'lbfgs'.

kappafloat, default: 1.96

Controls how much of the variance in the predicted values should betaken into account. If set to be very high, then we are favouringexploration over exploitation and vice versa.Used when the acquisition is 'LCB'.

xifloat, default: 0.01

Controls how much improvement one wants over the previous bestvalues. Used when the acquisition is either 'EI' or 'PI'.

noisefloat, default: 'gaussian'

• Use noise='gaussian' if the objective returns noisy observations.The noise of each observation is assumed to be iid withmean zero and a fixed variance.

• If the variance is known before-hand, this can be set directlyto the variance of the noise.

• Set this to a value close to zero (1e-10) if the function isnoise-free. Setting to zero might cause stability issues.

n_jobsint, default: 1

Number of cores to run in parallel while running the lbfgsoptimizations over the acquisition function. Valid onlywhen acq_optimizer is set to 'lbfgs'.Defaults to 1 core. If n_jobs=-1, then number of jobs is setto number of cores.

model_queue_sizeint or None, default: None

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

Returns

resOptimizeResult, 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.

• models: surrogate models used for each iteration.

• x_iters [list of lists]: location of function evaluation for eachiteration.

• func_vals [array]: function value for each iteration.

• space [Space]: the optimization space.

• specs [dict]`: the call specifications.

• rng [RandomState instance]: State of the random stateat the end of minimization.

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

See also

functions skopt.forest_minimize,skopt.dummy_minimize, skopt.gbrt_minimize

MinimizeToTray Plus 1.0.9

MinimizeToTray Plus is an extension that makes it possible to minimize Firefox, Thunderbird and other Mozilla based applications to the system tray.

Key Features.

• Minimize application to the system tray.
• Tray menu for preforming certain functions of the programs.
• Start the application minimized at computer startup.
• Ability to always show a tray icon for the application.
• Option to restore minimized windows if application is invoked by another program.
• A number of options to customize the extensions behaviour.
• Various ways of minimize the application; Menu item, keyboard shortcut, and Toolbar Button.
• Available in over 25 different translations/locales.

Please read the documentation for more detailed information.

Installing into Firefox, Flock, SeaMonkey and Songbird

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1. If a yellow bar appears, press the 'Allow' button.
2. In the window that pops up and once the button is enabled, press install.

Installing into Thunderbird, Sunbird and Postbox

1. Download and save the file to your hard disk.
2. In the application, open Add-ons from the Tools menu.
3. Click the Install button, and locate/select the file you downloaded and click 'OK'.
4. In the window that pops up and once the button is enabled, press install.

Install (For Firefox) • Download (For Thunderbird & Sunbird)

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Installation Help

Extension Information

• Name: MinimizeToTray PlusDownload
• Released: Feb. 5, 2010
• Version: 1.0.9
• Compatibility:
  • Thunderbird 3.0a1pre to 3.2a1pre
  • Postbox 1.0.0 to 1.1.*
  • SeaMonkey 2.0 to 2.1a1pre
• File size: 120.4 KB

Description

Minimizing Mozilla Firefox or Thunderbird to the tray. is the core feature of MinimizeToTray Plus. There are a number quick and flexible of ways it lets you do this. You can use the Minimize To Tray option in the File menu. You can press Ctrl+Shift+M from the keyboard. You can also check a box in the extensions options window to force applications to always minimize to the tray instead of the taskbar. There is also a toolbar button you could click.

Good option set to allow easy configuration. MinimizeToTray Plus has a number of options for controlling how the extension functions. You can set if the application should minimize to the tray instead of the task bar. It should restore on a single or double click. It should minimize the application on close. If it should restore windows instead of letting new ones open. Or limit the icons to a single one that always shows.

Load the application at startup. You can though the option window select to have the application, whether it be Firefox or Thunderbird to something else, to load at start up and in a minimized form. Only showing the tray icon.

Tray menus via right mouse click. MinimizeToTray provides quick access to many of Firefox and Thunderbird's most used features. For Firefox, easily view your Downloads or open a new browser window. For Thunderbird, compose new messages and view your Address Book while keeping it minimized all day to check for new mail.

Turbo option for Firefox and Thunderbird (and others). Turbo mode is when the extension is started with no visible windows, and only the tray icon showing. I can be used to keep the application running in the background, for an extremely fast start up or for applications like Thunderbird to allow it to carry out background tasks like continually checking you mail. It can be turned on either in the options window (always show icon) or with a command line flag -turbo.

Help & Support

Future Features

Enhancements that are planed for the extension. Please post to the support forums to ask for new features. A list is maintained in the Feature Requests thread.

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Translations

All translations for the extension are done through the MinimizeToTray Plus BabelZilla page by volentears. I encourage everyone to who can to contribute. It is greatly appreciated and the extension's users.

Source Code

The MinimizeToTray Plus source code is online for anyone who wants to view it - and even possibly provide a patch :)

Credits

A lot of credit for the extension should go to the original developers mook (Mark Yen) and Brad Peterson. Particularly mook who kindly put up with many questions from me on IRC when I was just starting out as an extension developer and long before I took any interest in MinimizeToTray.

Also thanks is owing to the people that have volunteered to translate the extension into a number of locals. They have done a great job!