normal
- random.normal(shape, mean, standard_deviation, seed=None, *, name=None)
Create a sample of normally distributed random numbers.
- Parameters:
shape (tuple[int, ...]) – The shape of the sampled random numbers.
mean (float) – The mean of the normal distribution.
standard_deviation (float) – The standard deviation of the normal distribution.
seed (int or None, optional) – A seed for the random number generator. Defaults to None, in which case a random value for the seed is used.
name (str or None, optional) – The name of the node.
- Returns:
A tensor containing a sample of normally distributed random numbers with shape
shape
.- Return type:
See also
Graph.random.choices
Create random samples from the data that you provide.
Graph.random.uniform
Create a sample of uniformly distributed random numbers.
boulderopal.run_stochastic_optimization
Function to find the minimum of generic stochastic functions.
Examples
Create a random tensor by sampling from a Gaussian distribution.
>>> samples = graph.random.normal( ... shape=(3, 1), mean=0.0, standard_deviation=0.05, seed=0, name="samples" ... ) >>> result = bo.execute_graph(graph=graph, output_node_names="samples") >>> result["output"]["samples"]["value"] array([[-0.03171833], [0.00816805], [-0.06874011]])
Create a batch of noise signals to construct a PWC Hamiltonian. The signal is defined as \(a \cos(\omega t)\), where \(a\) follows a normal distribution and \(\omega\) follows a uniform distribution.
>>> seed = 0 >>> batch_size = 3 >>> sigma_x = np.array([[0, 1], [1, 0]]) >>> sample_times = np.array([0.1, 0.2]) >>> a = graph.random.normal((batch_size, 1), mean=0.0, standard_deviation=0.05, seed=seed) >>> omega = graph.random.uniform( ... shape=(batch_size, 1), lower_bound=np.pi, upper_bound=2 * np.pi, seed=seed ... ) >>> sampled_signal = a * graph.cos(omega * sample_times[None]) >>> hamiltonian = graph.pwc_signal(sampled_signal, duration=0.2) * sigma_x >>> hamiltonian.name = "hamiltonian" >>> result = bo.execute_graph(graph=graph, output_node_names="hamiltonian") >>> result["output"]["hamiltonian"] { 'durations': array([0.1, 0.1]), 'values': array([ [ [[-0. , -0.02674376], [-0.02674376, -0. ]], [[-0. , -0.01338043], [-0.01338043, -0. ]] ], [ [[ 0. , 0.00691007], [ 0.00691007, 0. ]], [[ 0. , 0.00352363], [ 0.00352363, 0. ]]], [ [[-0. , -0.06230612], [-0.06230612, -0. ]], [[-0. , -0.04420857], [-0.04420857, -0. ]] ] ]), 'time_dimension': 1 }
See more examples in the How to optimize controls robust to strong noise sources user guide.