[−][src]Struct linregress::RegressionDataBuilder
A builder to create a RegressionData struct for use with a FormulaRegressionBuilder
.
Methods
impl RegressionDataBuilder
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pub fn new() -> Self
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Create a new RegressionDataBuilder
pub fn invalid_value_handling(self, setting: InvalidValueHandling) -> Self
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Configure how to handle non real f64
values (NaN or infinity or negative infinity) using
a variant of the InvalidValueHandling
enum.
The default value is ReturnError
.
Example
use linregress::{InvalidValueHandling, RegressionDataBuilder}; let builder = RegressionDataBuilder::new(); let builder = builder.invalid_value_handling(InvalidValueHandling::DropInvalid);
pub fn build_from<'a, I, S>(self, data: I) -> Result<RegressionData<'a>, Error> where
I: IntoIterator<Item = (S, Vec<f64>)>,
S: Into<Cow<'a, str>>,
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I: IntoIterator<Item = (S, Vec<f64>)>,
S: Into<Cow<'a, str>>,
Build a RegressionData
struct from the given data.
Any type that implements the IntoIterator
trait can be used for the data.
This could for example be a Hashmap
or a Vec
.
The iterator must consist of tupels of the form (S, Vec<f64>)
where
S
is a type that implements Into<Cow<str>>
, such as String
or str
.
You can think of this format as the representation of a table of data where
each tuple (S, Vec<f64>)
represents a column. The S
is the header or label of the
column and the Vec<f64>
contains the data of the column.
Example
use std::collections::HashMap; use linregress::RegressionDataBuilder; let builder = RegressionDataBuilder::new(); let mut data1 = HashMap::new(); data1.insert("Y", vec![1., 2., 3., 4.]); data1.insert("X", vec![4., 3., 2., 1.]); let regression_data1 = RegressionDataBuilder::new().build_from(data1)?; let y = vec![1., 2., 3., 4.]; let x = vec![4., 3., 2., 1.]; let data2 = vec![("X", x), ("Y", y)]; let regression_data2 = RegressionDataBuilder::new().build_from(data2)?;
Trait Implementations
impl Copy for RegressionDataBuilder
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impl Default for RegressionDataBuilder
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fn default() -> RegressionDataBuilder
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impl Clone for RegressionDataBuilder
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fn clone(&self) -> RegressionDataBuilder
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fn clone_from(&mut self, source: &Self)
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Performs copy-assignment from source
. Read more
impl Debug for RegressionDataBuilder
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Auto Trait Implementations
impl Send for RegressionDataBuilder
impl Sync for RegressionDataBuilder
Blanket Implementations
impl<T, U> Into for T where
U: From<T>,
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U: From<T>,
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
impl<T> From for T
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impl<T, U> TryFrom for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T> Borrow for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T, U> TryInto for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Same for T
type Output = T
Should always be Self
impl<SS, SP> SupersetOf for SP where
SS: SubsetOf<SP>,
SS: SubsetOf<SP>,