def test_assign_array(self):
# Network definition
g = nx.random_geometric_graph(10, 0.1)
m = Model(g)
initial_state = {
'x': 0,
}
def update_x():
return {'x': np.arange(10)}
# Model definition
m.set_states(['x'])
m.add_update(update_x)
m.set_initial_state(initial_state)
output = m.simulate(1)
self.assertEqual(list(output['states'][0]), list(np.zeros((10, 1))))
self.assertEqual(list(output['states'][1]), list(np.arange(10)))
I = model.get_state('I')
R = model.get_state('R')
dSdt, dIdt, dRdt = deriv(S, I, constants)
return {
# 'state': s + (0.01 * (-s + 1))
'S': S + (dt * dSdt),
'I': I + (dt * dIdt),
'R': R + (dt * dRdt)
}
# Model definition
model.set_states(['S', 'I', 'R'])
model.add_update(update, {'constants': constants})
model.set_initial_state(initial_state)
its = model.simulate(10000)
iterations = list(its['states'].values())
s = [v[0][0] for v in iterations]
i = [v[0][1] for v in iterations]
r = [v[0][2] for v in iterations]
import matplotlib.pyplot as plt
x = np.linspace(0, 100, 10001)
plt.figure()
plt.plot(x, s, label='S')
plt.plot(x, i, label='I')
plt.plot(x, r, label='R')
class HIOM(Example):
def __init__(self):
n = 400
g = nx.watts_strogatz_graph(n, 2, 0.02)
cfg = {
'utility': False,
}
self.model = Model(g, ModelConfiguration(cfg))
constants = {
'dt': 0.01,
'A_min': -0.5,
'A_star': 1,
's_O': 0.01,
's_I': 0,
'd_A': 0,
'p': 1,
'r_min': 0,
't_O': np.inf,
'N': n
}
def initial_I(constants):
return np.random.normal(0, 0.3, constants['N'])
def initial_O(constants):
return np.random.normal(0, 0.2, constants['N'])
initial_state = {
'I': initial_I,
'O': initial_O,
'A': 1
}
def update_I_A(nodes, constants):
node = nodes[0]
nb = np.random.choice(self.model.get_neighbors(node))
if abs(self.model.get_node_state(node, 'O') - self.model.get_node_state(nb, 'O')) > constants['t_O']:
return {'I': self.model.get_node_state(node, 'I')}
else:
# Update information
r = constants['r_min'] + (1 - constants['r_min']) / (1 + np.exp(-1 * constants['p'] * (self.model.get_node_state(node, 'O') - self.model.get_node_state(nb, 'O'))))
inf = r * self.model.get_node_state(node, 'I') + (1-r) * self.model.get_node_state(nb, 'I') + np.random.normal(0, constants['s_I'])
# Update attention
node_A = self.model.get_node_state(node, 'A') + constants['d_A'] * (2 * constants['A_star'] - self.model.get_node_state(node, 'A'))
nb_A = self.model.get_node_state(nb, 'A') + constants['d_A'] * (2 * constants['A_star'] - self.model.get_node_state(nb, 'A'))
return {'I': [inf], 'A': {node: node_A, nb: nb_A}}
def update_A(constants):
return {'A': self.model.get_state('A') - 2 * constants['d_A'] * self.model.get_state('A')/constants['N']}
def update_O(constants):
noise = np.random.normal(0, constants['s_O'], constants['N'])
x = self.model.get_state('O') - constants['dt'] * (self.model.get_state('O')**3 - (self.model.get_state('A') + constants['A_min']) * self.model.get_state('O') - self.model.get_state('I')) + noise
return {'O': x}
def shrink_I():
return {'I': self.model.get_state('I') * 0.999}
def shrink_A():
return {'A': self.model.get_state('A') * 0.999}
def sample_attention_weighted(graph):
probs = []
A = self.model.get_state('A')
factor = 1.0/sum(A)
for a in A:
probs.append(a * factor)
return np.random.choice(graph.nodes, size=1, replace=False, p=probs)
# Model definition
self.model.constants = constants
self.model.set_states(['I', 'A', 'O'])
update_cfg = UpdateConfiguration({
'arguments': {'constants': self.model.constants},
'get_nodes': True
})
up_I_A = Update(update_I_A, update_cfg)
s_I = Update(shrink_I)
s_A = Update(shrink_A)
self.model.add_scheme(Scheme(sample_attention_weighted, {'args': {'graph': self.model.graph}, 'updates': [up_I_A]}))
self.model.add_scheme(Scheme(lambda graph: graph.nodes, {'args': {'graph': self.model.graph}, 'lower_bound': 5000, 'updates': [s_I]}))
self.model.add_scheme(Scheme(lambda graph: graph.nodes, {'args': {'graph': self.model.graph}, 'lower_bound': 10000, 'updates': [s_A]}))
self.model.add_update(update_A, {'constants': self.model.constants})
self.model.add_update(update_O, {'constants': self.model.constants})
self.model.set_initial_state(initial_state, {'constants': self.model.constants})
def simulate(self, n):
return self.model.simulate(n)
def visualize(self, iterations):
visualization_config = {
'layout': 'fr',
'plot_interval': 100,
'plot_variable': 'O',
'variable_limits': {
'A': [0, 1],
'O': [-1, 1],
'I': [-1, 1]
},
'cmin': -1,
'cmax': 1,
'color_scale': 'RdBu',
'show_plot': True,
# 'plot_output': '../animations/HIOM.gif',
'plot_title': 'HIERARCHICAL ISING OPINION MODEL',
}
self.model.configure_visualization(visualization_config, iterations)
self.model.visualize('animation')
def update_lambda(constants):
return {'lambda': model.get_state('lambda') + 0.01}
def update_A(constants):
return {'A': constants['q'] * model.get_state('V') + np.minimum((np.random.poisson(model.get_state('lambda'))/7), constants['q']*(1 - model.get_state('V')))}
# Model definition
model.constants = constants
model.set_states(['C', 'S', 'E', 'V', 'lambda', 'A'])
model.add_update(update_C, {'constants': model.constants})
model.add_update(update_S, {'constants': model.constants})
model.add_update(update_E, {'constants': model.constants})
model.add_update(update_V, {'constants': model.constants})
model.add_update(update_lambda, {'constants': model.constants})
model.add_update(update_A, {'constants': model.constants})
model.set_initial_state(initial_state, {'constants': model.constants})
its = model.simulate(100)
iterations = its['states'].values()
A = [np.mean(it[:, 5]) for it in iterations]
C = [np.mean(it[:, 0]) for it in iterations]
E = [np.mean(it[:, 2]) for it in iterations]
lmd = [np.mean(it[:, 4]) for it in iterations]
S = [np.mean(it[:, 1]) for it in iterations]
V = [np.mean(it[:, 3]) for it in iterations]
x = np.arange(0, len(iterations))
class CravingSelfControl(Example):
def __init__(self):
# Network definition
self.g = nx.random_geometric_graph(250, 0.125)
cfg = {
'utility': False,
}
self.model = Model(self.g, ModelConfiguration(cfg))
constants = {
'q': 0.8,
'b': 0.5,
'd': 0.2,
'h': 0.2,
'k': 0.25,
'S+': 0.5,
}
constants['p'] = 2 * constants['d']
def initial_v(constants):
return np.minimum(
1,
np.maximum(
0,
self.model.get_state('C') - self.model.get_state('S') -
self.model.get_state('E')))
def initial_a(constants):
return constants['q'] * self.model.get_state('V') + (
np.random.poisson(self.model.get_state('lambda')) / 7)
initial_state = {
'C': 0,
'S': constants['S+'],
'E': 1,
'V': initial_v,
'lambda': 0.5,
'A': initial_a
}
def update_C(constants):
c = self.model.get_state(
'C') + constants['b'] * self.model.get_state('A') * np.minimum(
1, 1 - self.model.get_state('C')
) - constants['d'] * self.model.get_state('C')
return {'C': c}
def update_S(constants):
return {
'S':
self.model.get_state('S') + constants['p'] *
np.maximum(0, constants['S+'] - self.model.get_state('S')) -
constants['h'] * self.model.get_state('C') -
constants['k'] * self.model.get_state('A')
}
# Naive manner
# def update_E(constants):
# # return {'E': self.model.get_state('E') - 0.015}
# e = np.zeros(len(self.model.nodes))
# for i, node in enumerate(self.model.nodes):
# neighbor_addiction = 0
# for neighbor in self.model.get_neighbors(node):
# neighbor_addiction += self.model.get_node_state(neighbor, 'A')
# e[i] = neighbor_addiction / 50
# return {'E': np.maximum(-1.5, self.model.get_state('E') - e)} # Custom calculation
# Less naive
# def update_E(constants):
# e = np.zeros(len(self.model.nodes))
# adj = self.model.get_adjacency()
# for i in range(len(self.model.nodes)):
# neighbors = adj[i].nonzero()
# e[i] = np.sum(self.model.get_nodes_state(neighbors, 'A')) / 50
# return {'E': np.maximum(-1.5, self.model.get_state('E') - e)} # Custom calculation
def update_E(constants):
adj = self.model.get_adjacency()
summed = np.matmul(adj, self.model.get_nodes_states())
e = summed[:, self.model.get_state_index('A')] / 50
return {'E': np.maximum(-1.5, self.model.get_state('E') - e)}
def update_V(constants):
return {
'V':
np.minimum(
1,
np.maximum(
0,
self.model.get_state('C') - self.model.get_state('S') -
self.model.get_state('E')))
}
def update_lambda(constants):
return {'lambda': self.model.get_state('lambda') + 0.01}
def update_A(constants):
return {
'A':
constants['q'] * self.model.get_state('V') + np.minimum(
(np.random.poisson(self.model.get_state('lambda')) / 7),
constants['q'] * (1 - self.model.get_state('V')))
}
# Model definition
self.model.constants = constants
self.model.set_states(['C', 'S', 'E', 'V', 'lambda', 'A'])
self.model.add_update(update_C, {'constants': self.model.constants})
self.model.add_update(update_S, {'constants': self.model.constants})
self.model.add_update(update_E, {'constants': self.model.constants})
self.model.add_update(update_V, {'constants': self.model.constants})
self.model.add_update(update_lambda,
{'constants': self.model.constants})
self.model.add_update(update_A, {'constants': self.model.constants})
self.model.set_initial_state(initial_state,
{'constants': self.model.constants})
def simulate(self, n):
return self.model.simulate(n)
def plot_paper(self, iterations):
A = [np.mean(it[:, 5]) for it in iterations]
C = [np.mean(it[:, 0]) for it in iterations]
E = [np.mean(it[:, 2]) for it in iterations]
lmd = [np.mean(it[:, 4]) for it in iterations]
S = [np.mean(it[:, 1]) for it in iterations]
V = [np.mean(it[:, 3]) for it in iterations]
x = np.arange(0, len(iterations))
plt.figure()
plt.subplot(221)
plt.plot(x, E, label='E')
plt.plot(x, lmd, label='lambda')
plt.legend()
plt.subplot(222)
plt.plot(x, A, label='A')
plt.plot(x, C, label='C')
plt.legend()
plt.subplot(223)
plt.plot(x, S, label='S')
plt.plot(x, V, label='V')
plt.legend()
plt.show()
def visualize(self, iterations):
visualization_config = {
'plot_interval': 2,
'initial_positions': nx.get_node_attributes(self.g, 'pos'),
'plot_variable': 'A',
'color_scale': 'Reds',
'variable_limits': {
'A': [0, 0.8],
'lambda': [0.5, 1.5],
'C': [-1, 1],
'V': [-1, 1],
'E': [-1, 1],
'S': [-1, 1]
},
'show_plot': True,
# 'plot_output': './animations/c_vs_s.gif',
'plot_title': 'Self control vs craving simulation',
}
self.model.configure_visualization(visualization_config, iterations)
self.model.visualize('animation')
