Source code for mindspore.ops.operations.random_ops

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"""Operators for random."""

from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register
from .._utils import get_broadcast_shape


[docs]class StandardNormal(PrimitiveWithInfer): r""" Generates random numbers according to the standard Normal (or Gaussian) random number distribution. Args: seed (int): Random seed. Default: 0. seed2 (int): Random seed2. Default: 0. Inputs: - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed. Outputs: Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean and stddev. The dtype is float32. Examples: >>> shape = (4, 16) >>> stdnormal = P.StandardNormal(seed=2) >>> output = stdnormal(shape) """ @prim_attr_register def __init__(self, seed=0, seed2=0): """Init StandardNormal""" self.init_prim_io_names(inputs=['shape'], outputs=['output']) validator.check_value_type('seed', seed, [int], self.name) validator.check_value_type('seed2', seed2, [int], self.name) def __infer__(self, shape): shape_v = shape["value"] if shape_v is None: raise ValueError(f"For {self.name}, shape must be const.") validator.check_value_type("shape", shape_v, [tuple], self.name) for i, shape_i in enumerate(shape_v): validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name) out = { 'shape': shape_v, 'dtype': mstype.float32, 'value': None} return out
[docs]class Laplace(PrimitiveWithInfer): r""" Generates random numbers according to the Laplace random number distribution. It is defined as: .. math:: \text{f}(x;μ,λ) = \frac{1}{2λ}\exp(-\frac{|x-μ|}{λ}), Args: seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers. Default: 0. Inputs: - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed. - **mean** (Tensor) - The mean μ distribution parameter, which specifies the location of the peak. With float32 data type. - **lambda_param** (Tensor) - The parameter used for controling the variance of this random distribution. The variance of Laplace distribution is equal to twice the square of lambda_param. With float32 data type. Outputs: Tensor, has the shape 'shape' input and dtype as float32. Examples: >>> shape = (4, 16) >>> mean = Tensor(1.0, mstype.float32) >>> lambda_param = Tensor(1.0, mstype.float32) >>> laplace = P.Laplace(seed=2) >>> output = laplace(shape, mean, lambda_param) """ @prim_attr_register def __init__(self, seed=0): """Init Laplace""" self.init_prim_io_names(inputs=['shape', 'mean', 'lambda_param'], outputs=['output']) validator.check_value_type('seed', seed, [int], self.name) def __infer__(self, shape, mean, lambda_param): shape_v = shape["value"] if shape_v is None: raise ValueError(f"For {self.name}, shape must be const.") validator.check_value_type("shape", shape_v, [tuple], self.name) for i, shape_i in enumerate(shape_v): validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name) validator.check_tensor_type_same({"mean": mean["dtype"]}, [mstype.float32], self.name) validator.check_tensor_type_same({"lambda_param": lambda_param["dtype"]}, [mstype.float32], self.name) broadcast_shape = get_broadcast_shape(mean['shape'], lambda_param['shape'], self.name) broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name) out = { 'shape': broadcast_shape, 'dtype': mstype.float32, 'value': None} return out
[docs]class Gamma(PrimitiveWithInfer): r""" Produces random positive floating-point values x, distributed according to probability density function: .. math:: \text{P}(x|α,β) = \frac{\exp(-x/β)}{{β^α}\cdot{\Gamma(α)}}\cdot{x^{α-1}}, Args: seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers. Default: 0. Inputs: - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed. - **alpha** (Tensor) - The α distribution parameter. It is also known as the shape parameter. With float32 data type. - **beta** (Tensor) - The β distribution parameter. It is also known as the scale parameter. With float32 data type. Outputs: Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of alpha and beta. The dtype is float32. Examples: >>> shape = (4, 16) >>> alpha = Tensor(1.0, mstype.float32) >>> beta = Tensor(1.0, mstype.float32) >>> gamma = P.Gamma(seed=3) >>> output = Gamma(shape, alpha, beta) """ @prim_attr_register def __init__(self, seed=0): """Init Gamma""" self.init_prim_io_names(inputs=['shape', 'alpha', 'beta'], outputs=['output']) validator.check_value_type('seed', seed, [int], self.name) def __infer__(self, shape, alpha, beta): shape_v = shape["value"] if shape_v is None: raise ValueError(f"For {self.name}, shape must be const.") validator.check_value_type("shape", shape_v, [tuple], self.name) for i, shape_i in enumerate(shape_v): validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name) validator.check_tensor_type_same({"alpha": alpha["dtype"]}, [mstype.float32], self.name) validator.check_tensor_type_same({"beta": beta["dtype"]}, [mstype.float32], self.name) broadcast_shape = get_broadcast_shape(alpha['shape'], beta['shape'], self.name) broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name) out = { 'shape': broadcast_shape, 'dtype': mstype.float32, 'value': None} return out
[docs]class Poisson(PrimitiveWithInfer): r""" Produces random non-negative integer values i, distributed according to discrete probability function: .. math:: \text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!}, Args: seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers. Default: 0. Inputs: - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed. - **mean** (Tensor) - μ parameter the distribution was constructed with. The parameter defines mean number of occurrences of the event. With float32 data type. Outputs: Tensor. The shape should be the broadcasted shape of Input "shape" and shape of mean. The dtype is int32. Examples: >>> shape = (4, 16) >>> mean = Tensor(5.0, mstype.float32) >>> poisson = P.Poisson(seed=5) >>> output = poisson(shape, mean) """ @prim_attr_register def __init__(self, seed=0): """Init Poisson""" self.init_prim_io_names(inputs=['shape', 'mean'], outputs=['output']) validator.check_value_type('seed', seed, [int], self.name) def __infer__(self, shape, mean): shape_v = shape["value"] if shape_v is None: raise ValueError(f"For {self.name}, shape must be const.") validator.check_value_type("shape", shape_v, [tuple], self.name) for i, shape_i in enumerate(shape_v): validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name) validator.check_tensor_type_same({"mean": mean["dtype"]}, [mstype.float32], self.name) broadcast_shape = get_broadcast_shape(mean['shape'], shape_v, self.name) out = { 'shape': broadcast_shape, 'dtype': mstype.int32, 'value': None} return out
[docs]class UniformInt(PrimitiveWithInfer): r""" Produces random integer values i, uniformly distributed on the closed interval [a, b], that is, distributed according to the discrete probability function: .. math:: \text{P}(i|a,b) = \frac{1}{b-a+1}, Note: The number in tensor a should be strictly less than b at any position after broadcasting. Args: seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers. Default: 0. Inputs: - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed. - **a** (Tensor) - The a distribution parameter. It defines the minimum possibly generated value. With int32 data type. - **b** (Tensor) - The b distribution parameter. It defines the maximum possibly generated value. With int32 data type. Outputs: Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of a and b. The dtype is int32. Examples: >>> shape = (4, 16) >>> a = Tensor(1, mstype.int32) >>> b = Tensor(5, mstype.int32) >>> uniform_int = P.UniformInt(seed=10) >>> output = uniform_int(shape, a, b) """ @prim_attr_register def __init__(self, seed=0): """Init UniformInt""" self.init_prim_io_names(inputs=['shape', 'a', 'b'], outputs=['output']) validator.check_value_type('seed', seed, [int], self.name) def __infer__(self, shape, a, b): shape_v = shape["value"] if shape_v is None: raise ValueError(f"For {self.name}, shape must be const.") validator.check_value_type("shape", shape_v, [tuple], self.name) for i, shape_i in enumerate(shape_v): validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name) validator.check_tensor_type_same({"a": a["dtype"]}, [mstype.int32], self.name) validator.check_tensor_type_same({"b": b["dtype"]}, [mstype.int32], self.name) broadcast_shape = get_broadcast_shape(a['shape'], b['shape'], self.name) broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name) out = { 'shape': broadcast_shape, 'dtype': mstype.int32, 'value': None} return out
[docs]class UniformReal(PrimitiveWithInfer): r""" Produces random floating-point values i, uniformly distributed on the interval [min(a, b), max(a, b)), that is,\ distributed according to the probability density function: .. math:: \text{P}(i|a,b) = \frac{1}{b-a}, Args: seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers. Default: 0. Inputs: - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed. - **a** (Tensor) - The a distribution parameter. It defines the minimum possibly generated value. With float32 data type. - **b** (Tensor) - The b distribution parameter. It defines the maximum possibly generated value. With float32 data type. Outputs: Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of a and b. The dtype is float32. Examples: >>> shape = (4, 16) >>> a = Tensor(1.0, mstype.float32) >>> b = Tensor(5.0, mstype.float32) >>> uniform_real = P.UniformReal(seed=10) >>> output = uniform_real(shape, a, b) """ @prim_attr_register def __init__(self, seed=0): """Init UniformReal""" self.init_prim_io_names(inputs=['shape', 'a', 'b'], outputs=['output']) validator.check_value_type('seed', seed, [int], self.name) def __infer__(self, shape, a, b): shape_v = shape["value"] if shape_v is None: raise ValueError(f"For {self.name}, shape must be const.") validator.check_value_type("shape", shape_v, [tuple], self.name) for i, shape_i in enumerate(shape_v): validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name) validator.check_tensor_type_same({"a": a["dtype"]}, [mstype.float32], self.name) validator.check_tensor_type_same({"b": b["dtype"]}, [mstype.float32], self.name) broadcast_shape = get_broadcast_shape(a['shape'], b['shape'], self.name) broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name) out = { 'shape': broadcast_shape, 'dtype': mstype.float32, 'value': None} return out
[docs]class RandomChoiceWithMask(PrimitiveWithInfer): """ Generates a random samply as index tensor with a mask tensor from a given tensor. The input must be a tensor of rank >= 1. If its rank >= 2, the first dimension specify the number of sample. The index tensor and the mask tensor have the fixed shapes. The index tensor denotes the index of the nonzero sample, while the mask tensor denotes which elements in the index tensor are valid. Args: count (int): Number of items expected to get and the number should be greater than 0. Default: 256. seed (int): Random seed. Default: 0. seed2 (int): Random seed2. Default: 0. Inputs: - **input_x** (Tensor[bool]) - The input tensor. The input tensor rank should be >= 1 and <= 5. Outputs: Two tensors, the first one is the index tensor and the other one is the mask tensor. - **index** (Tensor) - The output shape is 2-D. - **mask** (Tensor) - The output shape is 1-D. Examples: >>> rnd_choice_mask = P.RandomChoiceWithMask() >>> input_x = Tensor(np.ones(shape=[240000, 4]).astype(np.bool)) >>> output_y, output_mask = rnd_choice_mask(input_x) """ @prim_attr_register def __init__(self, count=256, seed=0, seed2=0): """Init RandomChoiceWithMask""" validator.check_value_type("count", count, [int], self.name) validator.check_integer("count", count, 0, Rel.GT, self.name) validator.check_value_type('seed', seed, [int], self.name) validator.check_value_type('seed2', seed2, [int], self.name) def infer_shape(self, x_shape): validator.check_integer("input_x rank", len(x_shape), 1, Rel.GE, self.name) validator.check_integer("input_x rank", len(x_shape), 5, Rel.LE, self.name) return ([self.count, len(x_shape)], [self.count]) def infer_dtype(self, x_dtype): validator.check_tensor_type_same({'x': x_dtype}, [mstype.bool_], self.name) return (mstype.int32, mstype.bool_)
[docs]class RandomCategorical(PrimitiveWithInfer): """ Generates random samples from a given categorical distribution tensor. Args: dtype (mindspore.dtype): The type of output. Its value should be one of [mindspore.int16, mindspore.int32, mindspore.int64]. Default: mindspore.int64. Inputs: - **logits** (Tensor) - The input tensor. 2-D Tensor with shape [batch_size, num_classes]. - **num_sample** (int) - Number of sample to be drawn. Only constant values is allowed. - **seed** (int) - Random seed. Default: 0. Only constant values is allowed. Outputs: - **output** (Tensor) - The output Tensor with shape [batch_size, num_samples]. Examples: >>> class Net(nn.Cell): >>> def __init__(self, num_sample): >>> super(Net, self).__init__() >>> self.random_categorical = P.RandomCategorical(mindspore.int64) >>> self.num_sample = num_sample >>> def construct(self, logits, seed=0): >>> return self.random_categorical(logits, self.num_sample, seed) >>> >>> x = np.random.random((10, 5)).astype(np.float32) >>> net = Net(8) >>> output = net(Tensor(x)) """ @prim_attr_register def __init__(self, dtype=mstype.int64): """Init RandomCategorical""" self.dtype = dtype valid_values = (mstype.int32, mstype.int16, mstype.int64) validator.check_type_name("dtype", dtype, valid_values, self.name) self.init_prim_io_names(inputs=['logits', 'num_samples', 'seed'], outputs=['output']) def __infer__(self, logits, num_samples, seed): logits_dtype = logits['dtype'] valid_types = (mstype.float32, mstype.float16, mstype.float64) validator.check_tensor_type_same({'logits': logits_dtype}, valid_types, self.name) num_samples_v = num_samples['value'] seed_v = seed['value'] validator.check_value_type('num_samples', num_samples_v, (int,), self.name) validator.check_value_type('seed', seed_v, (int,), self.name) validator.check_integer("num_samples", num_samples_v, 0, Rel.GT, self.name) x_shape = list(logits['shape']) if len(x_shape) != 2: raise ValueError("RandomCategorical shape should be 2-dimension.") ndim = len(x_shape) - 1 x_shape[ndim] = num_samples_v return {'shape': (x_shape), 'dtype': (self.dtype), 'value': None}
[docs]class Multinomial(PrimitiveWithInfer): r""" Returns a tensor sampled from the multinomial probability distribution located in the corresponding row of tensor input. Note: The rows of input do not need to sum to one (in which case we use the values as weights), but must be non-negative, finite and have a non-zero sum. Args: seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers. Default: 0. Inputs: - **input** (Tensor[float32]) - the input tensor containing the cumsum of probabilities, must be 1 or 2 dims. - **num_samples** (int) - number of samples to draw. Outputs: Tensor. have the same rows with input, each row has num_samples sampled indices. Examples: >>> input = Tensor([0., 9., 4., 0.], mstype.float32) >>> multinomial = P.Multinomial(seed=10) >>> output = multinomial(input, 2) """ @prim_attr_register def __init__(self, seed=0): """init""" validator.check_value_type("seed", seed, [int], self.name) self.init_prim_io_names(inputs=['input', 'num_sample'], outputs=['output']) def __infer__(self, inputs, num_samples): input_shape = inputs["shape"] if len(input_shape) != 1 and len(input_shape) != 2: raise ValueError("input dim must be 1 or 2") validator.check_tensor_type_same({'inputs': inputs['dtype']}, [mstype.float32], self.name) num_samples_value = num_samples["value"] if num_samples_value is None: raise ValueError(f"For {self.name}, shape nust be const") validator.check_value_type("num_samples", num_samples_value, [int], self.name) validator.check_integer("num_samples", num_samples_value, 0, Rel.GT, None) y_shape = (num_samples_value,) if len(input_shape) == 2: y_shape = (input_shape[0], num_samples_value) out = { "shape": y_shape, "dtype": mstype.int32, "value": None} return out