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# Random

## bernoulli

INDArray bernoulli(double p, DataType datatype, long[] shape)
SDVariable bernoulli(double p, DataType datatype, long[] shape)
SDVariable bernoulli(String name, double p, DataType datatype, long[] shape)
Generate a new random INDArray, where values are randomly sampled according to a Bernoulli distribution,
with the specified probability. Array values will have value 1 with probability P and value 0 with probability
1-P.
• p - Probability of value 1
• datatype - Data type of the output variable
• shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

## binomial

INDArray binomial(int nTrials, double p, DataType datatype, long[] shape)
SDVariable binomial(int nTrials, double p, DataType datatype, long[] shape)
SDVariable binomial(String name, int nTrials, double p, DataType datatype, long[] shape)
Generate a new random INDArray, where values are randomly sampled according to a Binomial distribution,
with the specified number of trials and probability.
• nTrials - Number of trials parameter for the binomial distribution
• p - Probability of success for each trial
• datatype - Data type of the output variable
• shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

## exponential

INDArray exponential(double lambda, DataType datatype, long[] shape)
SDVariable exponential(double lambda, DataType datatype, long[] shape)
SDVariable exponential(String name, double lambda, DataType datatype, long[] shape)
Generate a new random INDArray, where values are randomly sampled according to a exponential distribution:
P(x) = lambda exp(-lambda x)
• lambda - lambda parameter
• datatype - Data type of the output variable
• shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

## logNormal

INDArray logNormal(double mean, double stddev, DataType datatype, long[] shape)
SDVariable logNormal(double mean, double stddev, DataType datatype, long[] shape)
SDVariable logNormal(String name, double mean, double stddev, DataType datatype, long[] shape)
Generate a new random INDArray, where values are randomly sampled according to a Log Normal distribution,
i.e., `log(x) ~ N(mean, stdev)`
• mean - Mean value for the random array
• stddev - Standard deviation for the random array
• datatype - Data type of the output variable
• shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

## normal

INDArray normal(double mean, double stddev, DataType datatype, long[] shape)
SDVariable normal(double mean, double stddev, DataType datatype, long[] shape)
SDVariable normal(String name, double mean, double stddev, DataType datatype, long[] shape)
Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev)
• mean - Mean value for the random array
• stddev - Standard deviation for the random array
• datatype - Data type of the output variable
• shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

## normalTruncated

INDArray normalTruncated(double mean, double stddev, DataType datatype, long[] shape)
SDVariable normalTruncated(double mean, double stddev, DataType datatype, long[] shape)
SDVariable normalTruncated(String name, double mean, double stddev, DataType datatype, long[] shape)
Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev). However, any values more than 1 standard deviation from the mean are dropped and re-sampled
• mean - Mean value for the random array
• stddev - Standard deviation for the random array
• datatype - Data type of the output variable
• shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))

## uniform

INDArray uniform(double min, double max, DataType datatype, long[] shape)
SDVariable uniform(double min, double max, DataType datatype, long[] shape)
SDVariable uniform(String name, double min, double max, DataType datatype, long[] shape)
Generate a new random INDArray, where values are randomly sampled according to a uniform distribution,
U(min,max)
• min - Minimum value
• max - Maximum value.
• datatype - Data type of the output variable
• shape - Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))