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time_courses.py
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238 lines (200 loc) · 11.2 KB
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import BehavioralOrganization as BehOrg
import DynamicField
import numpy
import math
import matplotlib.pyplot as plt
from matplotlib import rc
from enthought.mayavi import mlab
from mpl_toolkits.axes_grid import ImageGrid
from mpl_toolkits.axes_grid import make_axes_locatable
import plot_settings
import math_tools
def main():
task_node = DynamicField.DynamicField([], [], None)
field_sizes = [80, 80]
int_weight_0 = math_tools.gauss_2d(field_sizes, amplitude=10, sigmas=[5.0, 5.0], shifts=[20, 20])
int_weight_1 = math_tools.gauss_2d(field_sizes, amplitude=10, sigmas=[5.0, 5.0], shifts=[40, 50])
elem_behavior_0 = BehOrg.ElementaryBehavior.with_internal_fields(field_dimensionality=2,
field_sizes=[[field_sizes[0]],[field_sizes[1]]],
field_resolutions=[],
int_node_to_int_field_weight=int_weight_0,
int_node_to_cos_node_weight=2.0,
int_field_to_cos_field_weight=3.5,
cos_field_to_cos_node_weight=3.0,
cos_node_to_cos_memory_node_weight=2.5,
int_inhibition_weight=-6.0,
reactivating=False,
name="eb0")
elem_behavior_1 = BehOrg.ElementaryBehavior.with_internal_fields(field_dimensionality=2,
field_sizes=[[field_sizes[0]],[field_sizes[1]]],
field_resolutions=[],
int_node_to_int_field_weight=int_weight_1,
int_node_to_cos_node_weight=2.0,
int_field_to_cos_field_weight=3.5,
cos_field_to_cos_node_weight=3.0,
cos_node_to_cos_memory_node_weight=2.5,
int_inhibition_weight=-6.0,
reactivating=False,
name="eb1")
BehOrg.connect_to_task(task_node, elem_behavior_0)
BehOrg.connect_to_task(task_node, elem_behavior_1)
competition_nodes = BehOrg.competition(elem_behavior_0, elem_behavior_1, task_node, bidirectional=True)
time_steps = 1000
task_node_activation = [0] * time_steps
eb0_intention_node_activation = [0] * time_steps
eb0_intention_field_activation = [0] * time_steps
eb0_cos_node_activation = [0] * time_steps
eb0_cos_field_activation = [0] * time_steps
eb0_cos_memory_node_activation = [0] * time_steps
eb0_intention_field_activation_1d = numpy.zeros((time_steps, field_sizes[1]))
eb0_cos_field_activation = [0] * time_steps
eb0_cos_field_activation_1d = numpy.zeros((time_steps, field_sizes[1]))
competition_node_01_activation = [0] * time_steps
eb1_intention_node_activation = [0] * time_steps
eb1_intention_field_activation = [0] * time_steps
eb1_cos_node_activation = [0] * time_steps
eb1_cos_field_activation = [0] * time_steps
eb1_cos_memory_node_activation = [0] * time_steps
eb1_intention_field_activation_1d = numpy.zeros((time_steps, field_sizes[1]))
eb1_cos_field_activation = [0] * time_steps
eb1_cos_field_activation_1d = numpy.zeros((time_steps, field_sizes[1]))
competition_node_10_activation = [0] * time_steps
print_output = False
for i in range(time_steps):
if (i > 1):
task_node.set_boost(10)
if (i > 200):
elem_behavior_0.get_cos_field().set_boost(2.5)
if (i > 350):
elem_behavior_0.get_cos_field().set_boost(0.0)
if (i > 550):
elem_behavior_1.get_cos_field().set_boost(2.5)
if (i > 650):
elem_behavior_1.get_cos_field().set_boost(0.0)
task_node.step()
elem_behavior_0.step()
competition_nodes[0].step()
competition_nodes[1].step()
elem_behavior_1.step()
if (print_output is True):
print("task node activation (boost: " + str(task_node.get_boost()) + ")")
task_node_activation[i] = task_node.get_activation()[0]
if (print_output is True):
print(task_node_activation[i])
if (print_output is True):
print("int node activation")
eb0_intention_node_activation[i] = elem_behavior_0.get_intention_node().get_activation()[0]
if (print_output is True):
print(eb0_intention_node_activation[i])
if (print_output is True):
print("int field activation")
eb0_intention_field_activation[i] = elem_behavior_0.get_intention_field().get_activation()
if (print_output is True):
print(eb0_intention_field_activation[i])
if (print_output is True):
print("int field activation 1d")
eb0_intention_field_activation_1d[i] = eb0_intention_field_activation[i].max(0)
if (print_output is True):
print(eb0_intention_field_activation_1d)
if (print_output is True):
print("cos field activation (boost: " + str(elem_behavior_0.get_cos_field().get_boost()) + ")")
eb0_cos_field_activation[i] = elem_behavior_0.get_cos_field().get_activation()
if (print_output is True):
print(eb0_cos_field_activation[i])
if (print_output is True):
print("cos field activation 1d: ")
eb0_cos_field_activation_1d[i] = eb0_cos_field_activation[i].max(0)
if (print_output is True):
print(eb0_cos_field_activation_1d)
if (print_output is True):
print("cos node activation")
eb0_cos_node_activation[i] = elem_behavior_0.get_cos_node().get_activation()[0]
if (print_output is True):
print(eb0_cos_node_activation[i])
print("cos mem node activation")
eb0_cos_memory_node_activation[i] = elem_behavior_0.get_cos_memory_node().get_activation()[0]
if (print_output is True):
print(eb0_cos_memory_node_activation[i])
print("")
print("competition node 01 activation")
competition_node_01_activation[i] = competition_nodes[0].get_activation()[0]
if (print_output is True):
print(competition_node_01_activation[i])
print("competition node 10 activation")
competition_node_10_activation[i] = competition_nodes[1].get_activation()[0]
if (print_output is True):
print(competition_nodes[1].get_activation())
print("")
print("int node activation 1")
eb1_intention_node_activation[i] = elem_behavior_1.get_intention_node().get_activation()[0]
if (print_output is True):
print(elem_behavior_1.get_intention_node().get_activation())
print("int field activation 1")
eb1_intention_field_activation[i] = elem_behavior_1.get_intention_field().get_activation()
if (print_output is True):
print(elem_behavior_1.get_intention_field().get_activation())
print("int field activation 1 1d")
eb1_intention_field_activation_1d[i] = eb1_intention_field_activation[i].max(0)
if (print_output is True):
print(eb0_intention_field_activation_1d)
if (print_output is True):
print("cos field activation 1 (boost: " + str(elem_behavior_1.get_cos_field().get_boost()) + ")")
eb1_cos_field_activation[i] = elem_behavior_1.get_cos_field().get_activation()
if (print_output is True):
print(elem_behavior_1.get_cos_field().get_activation())
eb1_cos_field_activation_1d[i] = eb1_cos_field_activation[i].max(0)
if (print_output is True):
print(eb1_cos_field_activation_1d)
if (print_output is True):
print("cos node activation 1")
eb1_cos_node_activation[i] = elem_behavior_1.get_cos_node().get_activation()[0]
if (print_output is True):
print(elem_behavior_1.get_cos_node().get_activation())
print("cos mem node activation 1")
eb1_cos_memory_node_activation[i] = elem_behavior_1.get_cos_memory_node().get_activation()[0]
if (print_output is True):
print(elem_behavior_1.get_cos_memory_node().get_activation())
print("\n----------------------------------------------------\n")
plot_settings.set_mode("icdl")
fig = plt.figure(1)
fig.subplots_adjust(bottom=0.07, left=0.07, right=0.97, top=0.93)
plt.axes([0.125,0.2,0.95-0.125,0.95-0.2])
time_course_subplot = plt.subplot(2,1,1)
time_course_subplot.axes.grid(color='grey', linestyle='dotted')
plt.xlabel(r'time steps')
plt.ylabel(r'activation')
plt.plot(task_node_activation, 'k-', label=r'task')
plt.plot(eb0_intention_node_activation, 'r-', label=r'EB0 intention', antialiased=True)
plt.plot(eb0_cos_node_activation, 'r--', label=r'EB0 CoS', antialiased=True)
plt.plot(eb0_cos_memory_node_activation, 'r:', label=r'EB0 CoS mem', antialiased=True)
plt.plot(eb1_intention_node_activation, 'b-', label=r'EB1 intention', antialiased=True)
plt.plot(eb1_cos_node_activation, 'b--', label=r'EB1 CoS', antialiased=True)
plt.plot(eb1_cos_memory_node_activation, 'b:', label=r'EB1 CoS mem', antialiased=True)
plt.plot(competition_node_01_activation, 'g-.', label=r'competition 01', antialiased=True)
plt.plot(competition_node_10_activation, 'c-.', label=r'competition 10', antialiased=True)
plt.legend(loc='upper right')
plt.annotate('CoS EB0', xy=(200,-2), xytext=(110,-10), arrowprops=dict(arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"))
plt.annotate('CoS EB1', xy=(550,-2), xytext=(460,-10), arrowprops=dict(arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"))
grid = ImageGrid(fig, 212, nrows_ncols = (4,1), axes_pad=0.1, aspect=False)
grid[0].imshow(numpy.rollaxis(eb0_intention_field_activation_1d, 1), label='eb0 int field', aspect="auto", vmin=-10, vmax=10)
grid[0].invert_yaxis()
grid[0].set_yticks(range(0,field_sizes[0]+10,20))
grid[0].set_ylabel(r'EB0 int')
grid[1].imshow(numpy.rollaxis(eb0_cos_field_activation_1d, 1), label='eb0 CoS field', aspect="auto", vmin=-10, vmax=10)
grid[1].invert_yaxis()
grid[1].set_yticks(range(0,field_sizes[0]+10,20))
grid[1].set_ylabel(r'EB0 CoS')
grid[2].imshow(numpy.rollaxis(eb1_intention_field_activation_1d, 1), label='eb1 int field', aspect="auto", vmin=-10, vmax=10)
grid[2].invert_yaxis()
grid[2].set_yticks(range(0,field_sizes[0]+10,20))
grid[2].set_ylabel(r'EB1 int')
grid[3].imshow(numpy.rollaxis(eb1_cos_field_activation_1d, 1), label='eb1 CoS field', aspect="auto", vmin=-10, vmax=10)
grid[3].invert_yaxis()
grid[3].set_yticks(range(0,field_sizes[0]+10,20))
grid[3].set_ylabel(r'EB1 CoS')
grid[3].set_xlabel(r'time steps')
grid[3].set_xticks(range(0,time_steps+100,200))
plt.savefig("competition_plot.pdf", format="pdf")
plt.show()
if __name__ == "__main__":
main()