def __init__(self, n_inputs=4, n_agents=2, n_outputs=1):
"""
Initializes the network, including random connection strengths.
"""
# Defines the connection structure: half of incoming signals connect to one processing agent, half to the other.
# Both processing agents connect to the one final output node
split_pt = n_inputs / n_agents
self.connections = [[i, n_inputs + 1 * (split_pt > i)]
for i in range(n_inputs)]
self.connections.extend([[i, n_inputs + n_agents]
for i in range(n_inputs, n_inputs + n_agents)
])
self.n_inputs = n_inputs
self.n_agents = n_agents
# Creates agents based on this connection structure
self.agents = [agents.InputAgent(agent_id=i) for i in range(n_inputs)]
self.agents.extend([
agents.LinearSRS(agent_id=n_inputs + i,
n_connections_in=1 +
np.sum(np.asarray(self.connections)[:, 1] == i))
for i in range(n_agents)
])
self.agents.extend([
agents.LinearSRS(agent_id=n_inputs + n_agents + i,
n_connections_in=1 + n_agents)
for i in range(n_outputs)
])
def __init__(self,
error_function=None,
n_inputs=4,
n_agents=2,
n_outputs=1):
"""
Initializes the network, including random connection strengths.
"""
super().__init__(error_function, n_agents)
# Defines the connection structure: half of incoming signals connect to one processing agent, half to the other.
# Both processing agents connect to the one final output node
split_pt = n_inputs / n_agents
self.connections = [[i, n_agents] for i in range(n_agents)]
self.n_inputs = n_inputs
self.n_agents = n_agents
# Creates agents based on this connection structure
self.agents = [
agents.LinearSRS(agent_id=i,
n_connections_in=1 +
np.sum(np.asarray(self.connections)[:, 1] == i))
for i in range(n_agents + n_inputs)
]
self.agents.extend([
agents.LinearSRS(agent_id=n_inputs + n_agents + i,
n_connections_in=1 + n_agents)
for i in range(n_outputs)
])
# Logs the error function for this network
self.error_function = error_function
def __init__(self,
error_function=None,
n_inputs=5,
n_agents=5,
n_outputs=1):
"""
Initializes the network as fully connected: each agent receives input from all inputs and all other previous agents.
"""
super().__init__(error_function, n_agents)
# Defines the connection structure: half of incoming signals connect to one processing agent, half to the other.
# Both processing agents connect to the one final output node
split_pt = n_inputs / n_agents
self.connections = [[i, n_inputs + 1] for i in range(n_inputs)]
self.connections.extend([[i, i + 1]
for i in range(n_inputs, n_inputs + n_agents)
])
self.connections.extend(
[[n_inputs + n_agents - 1, n_inputs + n_agents + n_outputs - 1]])
self.n_inputs = n_inputs
self.n_agents = n_agents
# Creates agents based on this connection structure
self.agents = [agents.InputAgent(agent_id=i) for i in range(n_inputs)]
self.agents.extend([
agents.LinearSRS(agent_id=n_inputs + i,
n_connections_in=1 + (n_inputs - 1) *
(i == n_inputs))
for i in range(n_agents + n_outputs)
])
def __init__(self,
error_function=error_propagation.propagate_signal_importance,
n_inputs=4,
n_agents=2,
n_outputs=1):
"""
Initializes the network.
"""
super().__init__(error_function, n_agents)
self.n_inputs = n_inputs
self.n_agents = n_agents
# Sets up the input agent connections: all input agents send information to all processing agents
self.connections = []
for processing_agent in range(n_agents):
self.connections.extend([[i, n_inputs + processing_agent]
for i in range(n_inputs)])
for output_agent in range(n_outputs):
self.connections.extend(
[[n_inputs + i, n_inputs + n_agents + output_agent]
for i in range(n_agents)])
# Creates input + processing units that are RELUs and output agents that are Linear
self.agents = [
agents.ReLUSRS(agent_id=i,
n_connections_in=1 +
np.sum(np.asarray(self.connections)[:, 1] == i))
for i in range(n_agents + n_inputs)
]
self.agents.extend([
agents.LinearSRS(agent_id=n_inputs + n_agents + i,
n_connections_in=1 + n_agents)
for i in range(n_outputs)
])
def __init__(self,
error_function=error_propagation.propagate_signal_importance,
n_inputs=4,
n_agents=2,
n_outputs=1):
"""
Initializes the network, including random connection strengths.
"""
super().__init__(error_function, n_agents)
# Defines the connection structure: half of incoming signals connect to one processing agent, half to the other.
# Both processing agents connect to the one final output node
split_pt = n_inputs / n_agents
self.connections = [[i, n_agents] for i in range(n_agents)]
self.n_inputs = n_inputs
self.n_agents = n_agents
# Creates input + processing units that are RELUs and output agents that are Linear
self.agents = [
agents.ReLUSRS(agent_id=i,
n_connections_in=1 +
np.sum(np.asarray(self.connections)[:, 1] == i))
for i in range(n_agents + n_inputs)
]
self.agents.extend([
agents.LinearSRS(agent_id=n_inputs + n_agents + i,
n_connections_in=1 + n_agents)
for i in range(n_outputs)
])
def __init__(self, n_agents=1):
"""
Initializes the network, specifying each agent and which agents connect to one another
"""
# TODO: Let's build in the definitions of an input node. That'll allow us to experiment with mutliple inputs,
# TODO: and test whether the errors we're seeing are exclusive to line-line networks.
self.agents = [
agents.LinearSRS(agent_id=i, n_connections_in=2)
for i in range(n_agents)
]
self.connections = [[i, i + 1] for i in range(n_agents - 1)]