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celinelee
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python_fn_calls

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np.random.binomial(n, p, size=shape)
getattr(scipy.sparse, self.format + "_matrix")
csx_matrix(binomial, dtype=self.dtype)
connection_pattern(self, node)
grad(self, inputs, gout)
inputs (gz,)
theano.gradient.grad_undefined(op=self, x_pos=0, x=n, comment=comment_n)
theano.gradient.grad_undefined(op=self, x_pos=1, x=p, comment=comment_p)
theano.gradient.disconnected_type()
infer_shape(self, fgraph, node, ins_shapes)
Binomial("csr", "float32")
Binomial("csc", "float32")
Binomial("csr", "float64")
Binomial("csc", "float64")
Multinomial(Op)
make_node(self, n, p)
tensor.as_tensor_variable(n)
as_sparse_variable(p)
gof.Apply(self, [n, p], [p.type()
perform(self, node, inputs, outputs)
inputs (out,)
_is_sparse(p)
p.copy()
range(p.shape[0])
np.random.multinomial(n, p.data[k:l])
range(p.shape[0])
np.random.multinomial(n[i], p.data[k:l])
grad(self, inputs, outputs_gradients)
infer_shape(self, fgraph, node, ins_shapes)
Multinomial()
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'getDoc.settings.production')
get_wsgi_application()
while(True)
deck.Deck()
p.shuffle()
p.deal(5)
len(pai)
pai.sort(key=lambda x:x.figure)
range(1, len(pai)
if(pai[i].suit == pai[i-1].suit and (pai[i].figure == pai[i-1].figure + 1 or pai[i].figure == 10 and pai[i-1].figure == 1)
if(x == True)
print(i,end="\t")
print()
_RunWithEliotContext(PClass)
field(type=str)
field()
field(type=str)
field()
__eq__(self, other)
__ne__(self, other)
__hash__(self)
hash(self.func)
__call__(self, *args, **kwargs)
Action.continue_task(task_id=self.task_id)
self.func(*args, **kwargs)
compute_with_trace(*args)
compute()
ID (with Action.continue_task)
start_action(action_type="dask:compute")
optimize(*args, optimizations=[_add_logging])
compute(*optimized, optimize_graph=False)
_add_logging(dsk, ignore=None)
current_action()
toposort(dsk)
simplify(k)
isinstance(k, str)
join(str(o)
callable(value)
simplify(key)
str(ctx.serialize_task_id()
callable(func)
get_dependencies(dsk, key)
tuple(args)
result.keys()
dsk.keys()
Field(object)
__init__(self, field_type, default=None)
self.match(default)
match(self, value)
isinstance(value, self.field_type)
type(value)
EventMetaClass(type)
__new__(cls, name, bases, attrs)
attrs.items()
isinstance(v, Field)
fields.append(v)
super(EventMetaClass, cls)
__new__(cls, name, bases, new_attrs)
Event(object)
__init__(self, **kwargs)
copy.deepcopy(self.__params__)
kwargs.iteritems()
self.__setattr__(k, v)
__getattr__(self, key)
type(self)
__setattr__(self, key, value)
super(Event, self)
__setattr__(key, value)
match(value)
type(self)