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Eduardo Gulias Davis egulias

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egulias / name=kernel.event_listener
Created August 27, 2014 22:19
Tag debug sample output for --filter name=kernel.event_listener
+--------------------------+-----------------------+------------------------------+
| Service | Tag | Attributes (name => value) |
+--------------------------+-----------------------+------------------------------+
| data_collector.router | kernel.event_listener | event => kernel.controller |
| | | method => onKernelController |
| | | |
| monolog.handler.console | kernel.event_listener | event => console.command |
| | | method => onCommand |
| | | priority => 255 |
| | | |
@egulias
egulias / double filter
Created August 27, 2014 22:21
Tag debug sample output for --filter name=kernel.event_listener --filter attribute_value=event,kernel.controller
+-----------------------+-----------------------+------------------------------+
| Service | Tag | Attributes (name => value) |
+-----------------------+-----------------------+------------------------------+
| data_collector.router | kernel.event_listener | event => kernel.controller |
| | | method => onKernelController |
| | | |
| acme.demo.listener | kernel.event_listener | event => kernel.controller |
| | | method => onKernelController |
| | | |
+-----------------------+-----------------------+------------------------------+
@egulias
egulias / ann_guts
Created October 19, 2014 17:46
This is an abstract preview of a saved artificial neural network for SymfonyCon Madrid 2014
neurons (num_inputs, activation_function, activation_steepness)=(0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (10, 5, 5.00000000000000000000e-01) (10, 5, 5.00000000000000000000e-01) (10, 5, 5.00000000000000000000e-01) (0, 5, 0.00000000000000000000e+00) (4, 5, 5.00000000000000000000e-01) (4, 5, 5.00000000000000000000e-01) (4, 5, 5.00000000000000000000e-01) (4, 5, 5.00000000000000000000e-01) (4, 5, 5.00000000000000000000e-01) (4, 5, 5.00000000000000000000e-01) (4, 5, 5.00000000000000000000e-01) (4, 5, 5.00000000000000000000e-01) (4, 5, 5.00000000000000000000e-01) (0, 5, 0.00000000000000000000e+00)
connections (connected_to_neuron, weight)=(0, 2.74120140075683593750e+00) (1, 1.17843687534332275391e+00) (2, 6.5268