import hypergraph as hg
import numpy as np
graph1 = hg.Graph()
with graph1.as_default():
hg.mark("abc1") << (hg.dump() << "** abc1 **")
n = hg.mark("abc2") << (hg.dump() << "** abc2 **")
idx = hg.node(lambda _: np.random.randint(0, 2))
hg.output() << (hg.select(idx) << ["abc1", "abc2"])
for _ in range(3):
ctx = hg.ExecutionContext()
with ctx.as_default():
print(graph1())
print("*** end of execution ***")
]
fit = hg.call(model.fit_generator) << {
'generator':
generator,
'epochs':
1,
'max_queue_size':
hg.tweak(hg.QUniform(low=2, high=64), name='max_queue_size'),
'workers':
hg.tweak(hg.QLogUniform(low=1, high=16), name='workers'),
'callbacks': [time_history],
'verbose':
0
}
fit << hg.deps(set_batch_size)
hg.output() << hg.call1(
lambda _: time_history.get_total_time()) << hg.deps(fit)
def objective(individual):
try:
return hg.run(graph1, tweaks=individual)
except:
# in case of exception we penalize the maximum
return np.inf
history = History()
best = hg.optimize(algo='genetic',
graph=graph1,
objective=objective,
callbacks=[ConsoleLog(), history],
import hypergraph as hg
def test1(x, y):
return x + y
graph1 = hg.Graph(name='g1')
with graph1.as_default():
hg.mark("x_value") << 5
hg.output() << (
hg.invoke() << [test1, {
'x': hg.node_ref("x_value"),
'y': 3
}])
# Note, in a more realistic scenario, the first param of the invoke, that is the function to be invoked,
# may be the result of another computation or tweak
print(hg.run(graph1))
import hypergraph as hg
graph1 = hg.Graph()
with graph1.as_default():
hg.output() << (hg.delay(units=2) << hg.input_all())
ctx = hg.ExecutionContext()
with ctx.as_default():
for i in range(5):
print(graph1(i))
import hypergraph as hg
def test1(x, y):
return x + y
graph1 = hg.Graph()
with graph1.as_default():
# Note: the dictionary items are automatically mapped to the arguments
hg.output() << (hg.call(test1) << {
'x': 2,
'y': 3
}) # another possibility is passing arg by position: [2, 3]
print(graph1())
print(graph1())
hg.ExecutionContext.reset_default()
import hypergraph as hg
from hypergraph.genetic import GeneticOperators
graph1 = hg.Graph(name="g1")
with graph1.as_default():
hg.output() << (hg.permutation(size=3) << list('abcdef'))
# create a population from graph's phenotype
genetic = GeneticOperators(graph1)
print(genetic.phenotype)
population = genetic.create_population(3)
print(population[:2])
# crossover two parent to get a new individual
child = genetic.crossover_uniform_multi_parents(population[:2])
print(child)
genetic.mutations(child, prob=0.5)
# apply mutations to an individual
print(child)
# use an individual as graph's tweaks
print(hg.run(graph1, tweaks=child))
import hypergraph as hg
from hypergraph.genetic import GeneticOperators
@hg.decl_tweaks(y=hg.tweaks.Uniform())
@hg.decl_tweaks(z=hg.tweaks.Normal(mean=10))
def test1(x, y, z):
return x + y + z
graph1 = hg.Graph(name='g1')
with graph1.as_default():
# Note: the dictionary items are automatically mapped to the arguments
hg.output() << (hg.call(test1) << {'x': 2})
genetic = GeneticOperators(graph1)
print(genetic.phenotype)
tweaks = genetic.create_population(1)[0]
print(tweaks)
ctx = hg.ExecutionContext(tweaks=tweaks)
with ctx.as_default():
print(graph1())
import hypergraph as hg
graph1 = hg.Graph()
with graph1.as_default():
hg.var('v1', initial_value='test1')
#hg.output() << (hg.var('v1') << hg.SetVar('test2'))
hg.output() << (hg.set_var('v1') << 'test2')
ctx = hg.ExecutionContext()
with ctx.as_default():
print(graph1())
print(ctx.get_var_value(graph1, 'v1'))
import hypergraph as hg
graph1 = hg.Graph()
with graph1.as_default():
d = hg.input_all().as_dict()
hg.output() << [d.keys(), d.values(), d.items()]
ctx = hg.ExecutionContext()
with ctx.as_default():
print(graph1({'a': 1, 'b': 2}))