Struct stats::OnlineStats

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pub struct OnlineStats { /* private fields */ }

Expand description

Online state for computing mean, variance and standard deviation.

Implementations§

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impl OnlineStats

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pub fn new() -> OnlineStats

Create initial state.

Population size, variance and mean are set to 0.

Examples found in repository ?

src/online.rs (line 165)

    fn from_iter<I: IntoIterator<Item = T>>(it: I) -> OnlineStats {
        let mut v = OnlineStats::new();
        v.extend(it);
        v
    }

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pub fn from_slice<T: ToPrimitive>(samples: &[T]) -> OnlineStats

Initializes variance from a sample.

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pub const fn mean(&self) -> f64

Return the current mean.

Examples found in repository ?

src/online.rs (line 158)

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    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        write!(f, "{:.10} +/- {:.10}", self.mean(), self.stddev())
    }

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pub fn stddev(&self) -> f64

Return the current standard deviation.

Examples found in repository ?

src/online.rs (line 158)

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    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        write!(f, "{:.10} +/- {:.10}", self.mean(), self.stddev())
    }

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pub fn variance(&self) -> f64

Return the current variance.

Examples found in repository ?

src/online.rs (line 74)

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    pub fn stddev(&self) -> f64 {
        self.variance().sqrt()
    }

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pub fn add<T: ToPrimitive>(&mut self, sample: T)

Add a new sample.

Examples found in repository ?

src/online.rs (line 103)

    pub fn add_null(&mut self) {
        self.add(0usize);
    }

    /// Returns the number of data points.
    #[inline]
    #[must_use]
    pub const fn len(&self) -> usize {
        self.size as usize
    }

    /// Returns if empty.
    #[inline]
    #[must_use]
    pub const fn is_empty(&self) -> bool {
        self.size == 0
    }
}

impl Commute for OnlineStats {
    #[inline]
    fn merge(&mut self, v: OnlineStats) {
        // Taken from: https://en.wikipedia.org/wiki/Standard_deviation#Combining_standard_deviations
        let (s1, s2) = (self.size as f64, v.size as f64);
        let meandiffsq = (self.mean - v.mean) * (self.mean - v.mean);

        self.size += v.size;

        //self.mean = ((s1 * self.mean) + (s2 * v.mean)) / (s1 + s2);
        /*
        below is the fused multiply add version of the statement above
        its more performant as we're taking advantage of a CPU instruction
        Note that fma appears to have issues on macOS per the flaky CI tests
        and it appears that clippy::suboptimal_flops lint that suggested
        this made a false-positive recommendation
        https://github.com/rust-lang/rust-clippy/issues/10003
        leaving on for now, as qsv is primarily optimized for Linux targets */
        self.mean = s1.mul_add(self.mean, s2 * v.mean) / (s1 + s2);

        self.q += v.q + meandiffsq * s1 * s2 / (s1 + s2);
    }
}

impl Default for OnlineStats {
    fn default() -> OnlineStats {
        OnlineStats {
            size: 0,
            mean: 0.0,
            q: 0.0,
        }
    }
}

impl fmt::Debug for OnlineStats {
    #[inline]
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        write!(f, "{:.10} +/- {:.10}", self.mean(), self.stddev())
    }
}

impl<T: ToPrimitive> FromIterator<T> for OnlineStats {
    #[inline]
    fn from_iter<I: IntoIterator<Item = T>>(it: I) -> OnlineStats {
        let mut v = OnlineStats::new();
        v.extend(it);
        v
    }
}

impl<T: ToPrimitive> Extend<T> for OnlineStats {
    #[inline]
    fn extend<I: IntoIterator<Item = T>>(&mut self, it: I) {
        for sample in it {
            self.add(sample);
        }
    }