# parameter_expressions Module

Defines functions that can be used in HyperDrive to describe a hyperparameter search space.

These functions are used to specify different types of hyperparameter distributions. The distributions are defined when you configure sampling for a hyperparameter sweep. For example, when you use the RandomParameterSampling class, you can choose to sample from a set of discrete values or a distribution of continuous values. In this case, you could use the choice function to generate a discrete set of values and uniform function to generate a distribution of continuous values.

For examples of using these functions, see the tutorial: https://docs.microsoft.com/azure/machine-learning/how-to-tune-hyperparameters.

## Functions

### choice

Specify a discrete set of options to sample from.

`choice(*options)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### lognormal

Specify a value drawn according to exp(normal(mu, sigma)).

The logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.

`lognormal(mu, sigma)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### loguniform

Specify a log uniform distribution.

A value is drawn according to exp(uniform(min_value, max_value)) so that the logarithm of the return value is uniformly distributed. When optimizing, this variable is constrained to the interval [exp(min_value), exp(max_value)]

`loguniform(min_value, max_value)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### normal

Specify a real value that is normally-distributed with mean mu and standard deviation sigma.

When optimizing, this is an unconstrained variable.

`normal(mu, sigma)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### qlognormal

Specify a value like round(exp(normal(mu, sigma)) / q) * q.

Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.

`qlognormal(mu, sigma, q)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### qloguniform

Specify a uniform distribution of the form round(exp(uniform(min_value, max_value) / q) * q.

This is suitable for a discrete variable with respect to which the objective is "smooth", and gets smoother with the size of the value, but which should be bounded both above and below.

`qloguniform(min_value, max_value, q)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### qnormal

Specify a value like round(normal(mu, sigma) / q) * q.

Suitable for a discrete variable that probably takes a value around mu, but is fundamentally unbounded.

`qnormal(mu, sigma, q)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### quniform

Specify a uniform distribution of the form round(uniform(min_value, max_value) / q) * q.

This is suitable for a discrete value with respect to which the objective is still somewhat "smooth", but which should be bounded both above and below.

`quniform(min_value, max_value, q)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### randint

Specify a set of random integers in the range [0, upper).

The semantics of this distribution is that there is no more correlation in the loss function between nearby integer values, as compared with more distant integer values. This is an appropriate distribution for describing random seeds for example. If the loss function is probably more correlated for nearby integer values, then you should probably use one of the "quantized" continuous distributions, such as either quniform, qloguniform, qnormal or qlognormal.

`randint(upper)`

#### Parameters

#### Returns

The stochastic expression.

#### Return type

### uniform

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