# 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))