def main():
print 'Initializing...'
Sim.initialize()
white = LinePopulation( params['white x mean'], params['white y mean'],
params['white x std dev'], params['white y std dev'], params['mean speak'],
params['mean learn'], params['mean innovation'], params['white mean loc'],
params['white loc sd'], params['white talk radius'], 'white' )
seasoned = LinePopulation( 0.0, 0.0, 0.0, 0.0, params['mean speak'],
params['mean learn'], params['mean innovation'], params['seasoned mean loc'],
params['seasoned loc sd'], params['seasoned talk radius'], 'seasoned' )
bozal0 = LinePopulation( params['bozal0 x mean'], params['bozal0 y mean'],
params['bozal0 x std dev'], params['bozal0 y std dev'], params['mean speak'],
params['mean learn'], params['mean innovation'], params['bozal mean loc'],
params['bozal loc sd'], params['bozal talk radius'], 'bozal 0')
bozal1 = LinePopulation( params['bozal1 x mean'], params['bozal1 y mean'],
params['bozal1 x std dev'], params['bozal1 y std dev'], params['mean speak'],
params['mean learn'], params['mean innovation'], params['bozal mean loc'],
params['bozal loc sd'], params['bozal talk radius'], 'bozal 1' )
for i in range(params['num white']):
white.new_agent()
for i in range(params['num slaves']):
if i%2 == 0:
bozal0.new_agent()
else:
bozal1.new_agent()
adjuster = PopAdjuster( white, seasoned, bozal0, bozal1, params['update period'],
params['pop schedule'], params['white pop targets'],
params['slave pop targets'], params['proportion seasoned'] )
Sim.activate( adjuster, adjuster.go() )
number = math.ceil(params['proportion seasoned']*white.num_agents())
adjuster.get_seasoned( number, white, bozal0, bozal1, seasoned )
plotter = Plotter('gradual', params['plot period'], params['stop time'], params)
Sim.activate( plotter, plotter.go() )
print 'Simulating...'
Sim.simulate(until=params['stop time'])
print 'Simulation Complete at t=%s' % Sim.now()
plotter.cleanup()
def main():
print 'Initializing...'
Sim.initialize()
Agent.lang_tolerance = params['lang tolerance']
white = LangRelPop( params['white x mean'], params['white y mean'],
params['white x std dev'], params['white y std dev'], params['mean speak'],
params['mean learn'], params['mean innovation'], params['white mean loc'],
params['white loc sd'], params['white talk radius'], 'white' )
seasoned = LangRelPop( params['seasoned x mean'], params['seasoned y mean'],
params['seasoned x std dev'], params['seasoned y std dev'], params['mean speak'],
params['mean learn'], params['mean innovation'], params['seasoned mean loc'],
params['seasoned loc sd'], params['seasoned talk radius'], 'seasoned' )
bozal0 = LangRelPop( params['bozal0 x mean'], params['bozal0 y mean'],
params['bozal0 x std dev'], params['bozal0 y std dev'], params['mean speak'],
params['mean learn'], params['mean innovation'], params['bozal mean loc'],
params['bozal loc sd'], params['bozal talk radius'], 'bozal 0')
bozal1 = LangRelPop( params['bozal1 x mean'], params['bozal1 y mean'],
params['bozal1 x std dev'], params['bozal1 y std dev'], params['mean speak'],
params['mean learn'], params['mean innovation'], params['bozal mean loc'],
params['bozal loc sd'], params['bozal talk radius'], 'bozal 1' )
for i in range(params['num white']):
white.new_agent()
for i in range(params['num seasoned']):
seasoned.new_agent()
for i in range(params['num bozal']):
if i%2 == 0:
bozal0.new_agent()
else:
bozal1.new_agent()
plotter = Plotter('centripetal by lang', params['plot period'], params['stop time'], params)
Sim.activate( plotter, plotter.go() )
print 'Simulating...'
Sim.simulate(until=params['stop time'])
print 'Simulation Complete at t=%s' % Sim.now()
plotter.cleanup()
def cpu_vs_gpu(epochs, train_data, validation_data, table_output_path = ''):
"""
This function can be used ot produce to compare the speed (in seconds) between
the usage of the gpu and cpu.
:param epochs: A list of epochs which should be used
:param train_data: The training data (obtained from splitter.py)
:param validation_data: The validation data (obtained from splitter.py)
:param table_output_path: The path to which the output should be written
"""
gpu_results = []
cpu_results = []
p = Plotter()
for epoch in epochs:
gpu_params = edit_params(['hm_epochs' ,'gpu'], [epoch, True])
cpu_params = edit_params(['hm_epochs' ,'gpu'], [epoch, False])
gpu_results.append(CNN().run_model(gpu_params, train_data, validation_data))
cpu_results.append(CNN().run_model(cpu_params, train_data, validation_data))
gpu_x, gpu_y = p.get_x_y(gpu_results, 'number_epochs', 'run_time')
cpu_x, cpu_y = p.get_x_y(cpu_results, 'number_epochs', 'run_time')
if table_output_path:
header = ['epoch','gpu_time','cpu_time']
write_to_file(table_output_path + 'gpu_vs_cpu.txt',header, [gpu_x, gpu_y, cpu_y])
p.double_line([gpu_x,cpu_x], [gpu_y, cpu_y], ['GPU','CPU'])
__author__ = 'matthew' from Mesh import Mesh from Solver import Solver from Plot import Plotter import Glob #Create mesh mesh = Mesh() #create solver and run loop solver = Solver(mesh=mesh) solver.solverLoop(mesh) #Create plotter and plot plotter = Plotter(mesh) plotter.plot()
print 'Initializing...'
Sim.initialize()
print 'Random seed: ' + repr(params['random seed'])
random.seed(params['random seed'])
pop0 = LinePopulation( 500.0, 500.0, params['initial x std dev'], params['initial y std dev'],
params['mean speak'], params['mean learn'], params['mean innovation'],
500.0, params['pop0 loc sd'], params['mean talk radius'] )
for i in range(params['num agents']):
pop0.new_agent()
segregator = Segregator()
Sim.activate( segregator, segregator.go() )
def simulate():
print 'Simulating...'
Sim.simulate(until=params['stop time'])
print 'Simulation Complete at t=%s' % Sim.now()
if __name__ == '__main__':
initialize()
plotter = Plotter('segregate', params['plot period'], params['stop time'], params)
Sim.activate( plotter, plotter.go() )
simulate()
plotter.cleanup()
