def test_convergence_plot():
agent_pos = [[0.5, 0.4, 0.3], [0.5, 0.4, 0.3]]
try:
convergence.plot(agent_pos[0], agent_pos[1], labels=1)
except:
convergence.plot(agent_pos[0], agent_pos[1], labels=["agent[0]", "agent[1]"])
try:
convergence.plot(agent_pos[0], agent_pos[1], labels=["agent[0]"])
except:
convergence.plot(agent_pos[0], agent_pos[1])
def test_convergence_plot():
new_history = history.History()
new_history.load('models/test.pkl')
agents = new_history.get(key='agents', index=(0, 0))
try:
convergence.plot(agents[0], agents[1], labels=1)
except:
convergence.plot(agents[0], agents[1], labels=['agent[0]', 'agent[1]'])
try:
convergence.plot(agents[0], agents[1], labels=['agent[0]'])
except:
convergence.plot(agents[0], agents[1])
import opytimizer.visualization.convergence as c
# Defines agent's position and fitness
agent_pos = [[0.5, 0.4, 0.3], [0.5, 0.4, 0.3]]
agent_fit = [0.5, 0.32, 0.18]
# Defines best agent's position and fitness
best_agent_pos = [[0.01, 0.005, 0.0001], [0.01, 0.005, 0.0001]]
best_agent_fit = [0.0002, 0.00005, 0.00002]
# Plotting the convergence of agent's positions
c.plot(agent_pos[0],
agent_pos[1],
labels=['$x_0$', '$x_1$'],
title='Sphere Function: $x^2 \mid x \in [-10, 10]$',
subtitle='Agent: 0 | Algorithm: Particle Swarm Optimization')
# Plotting the convergence of best agent's positions
c.plot(best_agent_pos[0],
best_agent_pos[1],
labels=['$x^*_0$', '$x^*_1$'],
title='Sphere Function: $x^2 \mid x \in [-10, 10]$',
subtitle="Agent: Best | Algorithm: Particle Swarm Optimization")
# Plotting the convergence of agent's and best agent's fitness
c.plot(agent_fit,
best_agent_fit,
labels=['$f(x)$', '$f(x^{*})$'],
title='Sphere Function: $x^2 \mid x \in [-10, 10]$',
subtitle="Agents: 0 and Best | Algorithm: Particle Swarm Optimization")
import opytimizer.visualization.convergence as c
# Defines agent's position and fitness
agent_pos = [[0.5, 0.4, 0.3], [0.5, 0.4, 0.3]]
agent_fit = [0.5, 0.32, 0.18]
# Defines best agent's position and fitness
best_agent_pos = [[0.01, 0.005, 0.0001], [0.01, 0.005, 0.0001]]
best_agent_fit = [0.0002, 0.00005, 0.00002]
# Plotting the convergence of agent's positions
c.plot(
agent_pos[0],
agent_pos[1],
labels=["$x_0$", "$x_1$"],
title="Sphere Function: $x^2 \mid x \in [-10, 10]$",
subtitle="Agent: 0 | Algorithm: Particle Swarm Optimization",
)
# Plotting the convergence of best agent's positions
c.plot(
best_agent_pos[0],
best_agent_pos[1],
labels=["$x^*_0$", "$x^*_1$"],
title="Sphere Function: $x^2 \mid x \in [-10, 10]$",
subtitle="Agent: Best | Algorithm: Particle Swarm Optimization",
)
# Plotting the convergence of agent's and best agent's fitness
c.plot(
agent_fit,