7 examples of 'float upto 2 decimal in python' in Python

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5def fmtFloat(n):
6 return '{:6.3f}'.format(n)
84def to_float(num):
85 """Convert anything to float."""
86 return float(to_num(num))
60def test_decimal_int(self):
61 a, b = mathfilters.handle_float_decimal_combinations(Decimal('2.0'), 1, '+')
62 self.assertTrue(isinstance(a, Decimal), 'Type is {0}'.format(type(a)))
63 self.assertTrue(isinstance(b, int), 'Type is {0}'.format(type(b)))
26def truncate_float(float_num, n):
27 str_repr = str(float_num)
28 idx = str_repr.find('.')
29 if idx > 0:
30 return float(str_repr[0: 1 + idx + n])
31 else:
32 return float_num
10def convert_to_decimal(number, frac=False):
11 bad_type = not isinstance(number, (int, long, str, unicode, Decimal, Fraction))
12 if bad_type:
13 message = "{}, of type {} is not suitable as a Decimal"
14 logger.warning(message.format(number, type(number)))
15 return Fraction(number) if frac else Decimal(number)
23def BN_convert_float(module):
24 '''
25 Designed to work with network_to_half.
26 BatchNorm layers need parameters in single precision.
27 Find all layers and convert them back to float. This can't
28 be done with built in .apply as that function will apply
29 fn to all modules, parameters, and buffers. Thus we wouldn't
30 be able to guard the float conversion based on the module type.
31 '''
32 if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
33 module.float()
34 for child in module.children():
35 BN_convert_float(child)
36 return module
14def makeFloat(sign, ds, tail):
15 return float((sign or '') + ds + tail)

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