cma.logger

module documentation

logger class mainly to be used with CMAEvolutionStrategy

Class	`CMADataLogger`	data logger for class `CMAEvolutionStrategy`.
Class	`Logger`	log an arbitrary number of data (a data row) per "timestep".
Class	`LoggerDummy`	use to fake a `Logger` in non-verbose setting
Function	`disp`	displays selected data from (files written by) the class `CMADataLogger`.
Function	`plot`	plot data from files written by a `CMADataLogger`, the call `cma.plot(name, argsdict)` is a shortcut for `cma.CMADataLogger(name).plot(argsdict)`
Function	`smartlogygrid`	turn on grid and also minor grid depending on y-limits
Variable	`__author__`	Undocumented
Variable	`last_figure_number`	Undocumented

def disp(name=None, idx=None): ¶

displays selected data from (files written by) the class CMADataLogger.

The call cma.disp(name, idx) is a shortcut for cma.CMADataLogger(name).disp(idx).

Arguments

name: name of the logger, filename prefix, None evaluates to the default 'outcma/cma'
idx: indices corresponding to rows in the data file; by default the first five, then every 100-th, and the last 10 rows. Too large index values are removed.

The best ever observed iteration is also printed by default.

Examples

import cma
from numpy import r_
# assume some data are available from previous runs
cma.disp(None, r_[0, -1])  # first and last
cma.disp(None, r_[0:int(1e9):100, -1]) # every 100-th and last
cma.disp(idx=r_[0, -10:0]) # first and ten last
cma.disp(idx=r_[0:int(1e9):1000, -10:0])

See Also
`CMADataLogger.disp`

def plot(name=None, fig=None, abscissa=1, iteridx=None, plot_mean=False, foffset=1e-19, x_opt=None, fontsize=7, downsample_to=3000.0, xsemilog=None, xnormalize=None, addcols=None, **kwargs): ¶

plot data from files written by a CMADataLogger, the call cma.plot(name, **argsdict) is a shortcut for cma.CMADataLogger(name).plot(**argsdict)

Arguments

name: name of the logger, filename prefix, None evaluates to the default 'outcma/cma'
fig: filename or figure number, or both as a tuple (any order)
abscissa: 0==plot versus iteration count, 1==plot versus function evaluation number
iteridx: iteration indices to plot
x_opt: if len(x_opt) == dimension, the difference to x_opt is plotted, otherwise the first row of x_opt are the indices of the variables to be plotted and the second row, if present, is used to take the difference.
xsemilog: customized semilog plot for x-values

Return None

Examples

cma.plot()  # the optimization might be still
            # running in a different shell
cma.s.figsave('fig325.png')
cma.s.figclose()

cdl = cma.CMADataLogger().downsampling().plot()
# in case the file sizes are large

Details

Data from codes in other languages (C, Java, Matlab, Scilab) have the same format and can be plotted just the same.

See Also
`CMADataLogger`, `CMADataLogger.plot`

def smartlogygrid(**kwargs): ¶

turn on grid and also minor grid depending on y-limits

__author__: str = ¶

Undocumented

last_figure_number: int = ¶

Undocumented