# [−][src]Struct statrs::distribution::Geometric

`pub struct Geometric { /* fields omitted */ }`

Implements the Geometric distribution

# Examples

```use statrs::distribution::{Geometric, Discrete};
use statrs::statistics::Mean;

let n = Geometric::new(0.3).unwrap();
assert_eq!(n.mean(), 1.0 / 0.3);
assert_eq!(n.pmf(1), 0.3);
assert_eq!(n.pmf(2), 0.21);```

## Methods

### `impl Geometric`[src]

#### `pub fn new(p: f64) -> Result<Geometric>`[src]

Constructs a new shifted geometric distribution with a probability of `p`

# Errors

Returns an error if `p` is not in `(0, 1]`

# Examples

```use statrs::distribution::Geometric;

let mut result = Geometric::new(0.5);
assert!(result.is_ok());

result = Geometric::new(0.0);
assert!(result.is_err());```

#### `pub fn p(&self) -> f64`[src]

Returns the probability `p` of the geometric distribution

# Examples

```use statrs::distribution::Geometric;

let n = Geometric::new(0.5).unwrap();
assert_eq!(n.p(), 0.5);```

## Trait Implementations

### `impl Univariate<u64, f64> for Geometric`[src]

#### `fn cdf(&self, x: f64) -> f64`[src]

Calculates the cumulative distribution function for the geometric distribution at `x`

# Formula

`1 - (1 - p) ^ (x + 1)`

### `impl Discrete<u64, f64> for Geometric`[src]

#### `fn pmf(&self, x: u64) -> f64`[src]

Calculates the probability mass function for the geometric distribution at `x`

# Formula

`(1 - p)^(x - 1) * p`

#### `fn ln_pmf(&self, x: u64) -> f64`[src]

Calculates the log probability mass function for the geometric distribution at `x`

# Formula

`ln((1 - p)^(x - 1) * p)`

### `impl Min<u64> for Geometric`[src]

#### `fn min(&self) -> u64`[src]

Returns the minimum value in the domain of the geometric distribution representable by a 64-bit integer

# Formula

`1`

### `impl Max<u64> for Geometric`[src]

#### `fn max(&self) -> u64`[src]

Returns the maximum value in the domain of the geometric distribution representable by a 64-bit integer

# Formula

`2^63 - 1`

### `impl Mean<f64> for Geometric`[src]

#### `fn mean(&self) -> f64`[src]

Returns the mean of the geometric distribution

# Formula

`1 / p`

### `impl Variance<f64> for Geometric`[src]

#### `fn variance(&self) -> f64`[src]

Returns the standard deviation of the geometric distribution

# Formula

`(1 - p) / p^2`

#### `fn std_dev(&self) -> f64`[src]

Returns the standard deviation of the geometric distribution

# Remarks

Returns `NAN` if `p` is `1`

# Formula

`sqrt(1 - p) / p`

### `impl Entropy<f64> for Geometric`[src]

#### `fn entropy(&self) -> f64`[src]

Returns the entropy of the geometric distribution

# Formula

`(-(1 - p) * log_2(1 - p) - p * log_2(p)) / p`

### `impl Skewness<f64> for Geometric`[src]

#### `fn skewness(&self) -> f64`[src]

Returns the skewness of the geometric distribution

# Formula

`(2 - p) / sqrt(1 - p)`

### `impl Median<f64> for Geometric`[src]

#### `fn median(&self) -> f64`[src]

Returns the median of the geometric distribution

# Remarks

Returns `1` if `p` is `1`

# Formula

`ceil(-1 / log_2(1 - p))`

### `impl Mode<u64> for Geometric`[src]

#### `fn mode(&self) -> u64`[src]

Returns the mode of the geometric distribution

# Formula

`1`

### `impl Clone for Geometric`[src]

#### `fn clone_from(&mut self, source: &Self)`1.0.0[src]

Performs copy-assignment from `source`. Read more

### `impl Distribution<f64> for Geometric`[src]

#### `fn sample_iter<R>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T> where    R: Rng, `[src]

Create an iterator that generates random values of `T`, using `rng` as the source of randomness. Read more

## Blanket Implementations

### `impl<T> ToOwned for T where    T: Clone, `[src]

#### `type Owned = T`

The resulting type after obtaining ownership.

### `impl<T, U> TryFrom<U> for T where    U: Into<T>, `[src]

#### `type Error = Infallible`

The type returned in the event of a conversion error.

### `impl<T, U> TryInto<U> for T where    U: TryFrom<T>, `[src]

#### `type Error = <U as TryFrom<T>>::Error`

The type returned in the event of a conversion error.