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- numpy.random.RandomState.standard_normal
...e Random sampling (numpy.random) Legacy random generation numpy.random.RandomState.standard_normal...
- numpy.random.RandomState.standard_normal (Python method, in numpy.random.RandomState.standard_normal)
- Legacy random generation
...rs = RandomState(12345) mt19937 = MT19937() mt19937.state = rs.get_state() rs2 = RandomState(mt19937) # Same output rs.standard_normal() rs2.standard_normal() rs.random() rs2.random() rs.standard_exponential() rs2.standard_exponential()...
- numpy.random.RandomState.lognormal
...random >>> # values, drawn from a normal distribution. >>> b = [] >>> for i in range(1000): ... a = 10. + np.random.standard_normal(100) ... b.append(np.prod(a)) >>> b = np.array(b) / np.min(b) # scale values to be positive >>> cou...
- numpy.random.RandomState.randn
...the “standard normal” distribution. Note This is a convenience function for users porting code from Matlab, and wraps standard_normal. That function takes a tuple to specify the size of the output, which is consistent with other NumPy fun...
- numpy.random.RandomState.standard_normal
...e Random sampling (numpy.random) Legacy random generation numpy.random.RandomState.standard_normal...
- numpy.random.RandomState.standard_t
...case of the hyperbolic distribution. As df gets large, the result resembles that of the standard normal distribution (standard_normal). Note New code should use the standard_t method of a Generator instance instead; please see the Quick...
- Random
Generator...stributions to choose from. Examples >>> from numpy.random import Generator, PCG64 >>> rng = Generator(PCG64()) >>> rng.standard_normal() -0.203 # random Accessing the BitGenerator and spawning bit_generator Gets the bit generator i...
- Random sampling (
numpy.random)...) 0.06369197489564249 # may vary # Generate an array of 10 numbers according to a unit Gaussian distribution >>> rng.standard_normal(10) array([-0.31018314, -1.8922078 , -0.3628523 , -0.63526532, 0.43181166, # may vary 0.5164...
- What’s new or different
...l and gamma generators use 256-step Ziggurat methods which are 2-10 times faster than NumPy’s default implementation in standard_normal, standard_exponential or standard_gamma. Because of the change in algorithms, it is not possible to repr...