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use anyhow::{Result,Context,ensure}; use crate::{MStats,RStats,Med,wsum,emsg}; impl RStats for Vec<i64> { /// Arithmetic mean of an i64 slice /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// assert_eq!(v1.amean().unwrap(),7.5_f64); /// ``` fn amean(&self) -> Result<f64> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"amean - sample is empty!")); Ok( self.iter().map(|&x| x as f64).sum::<f64>() / (n as f64) ) } /// Arithmetic mean and standard deviation of an i64 slice /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// let res = v1.ameanstd().unwrap(); /// assert_eq!(res.mean,7.5_f64); /// assert_eq!(res.std,4.031128874149275_f64); /// ``` fn ameanstd(&self) -> Result<MStats> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"ameanstd - sample is empty!")); let mut sx2 = 0_f64; let mean = self.iter().map(|&x|{ let lx = x as f64;sx2+=lx*lx; lx}).sum::<f64>() / (n as f64); Ok( MStats { mean : mean, std : (sx2 /(n as f64) - mean.powi(2)).sqrt() } ) } /// Linearly weighted arithmetic mean of an i64 slice. /// Linearly descending weights from n down to one. /// Time dependent data should be in the stack order - the last being the oldest. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// assert_eq!(v1.awmean().unwrap(),5.333333333333333_f64); /// ``` fn awmean(&self) -> Result<f64> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"awmean - sample is empty!")); let mut w = (n+1)as f64; // descending linear weights Ok( self.iter().map(|&x| { w -= 1.; w*x as f64 }).sum::<f64>() / wsum(n)) } /// Liearly weighted arithmetic mean and standard deviation of an i64 slice. /// Linearly descending weights from n down to one. /// Time dependent data should be in the stack order - the last being the oldest. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// let res = v1.awmeanstd().unwrap(); /// assert_eq!(res.mean,5.333333333333333_f64); /// assert_eq!(res.std,3.39934634239519_f64); /// ``` fn awmeanstd(&self) -> Result<MStats> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"awmeanstd - sample is empty!")); let mut sx2 = 0_f64; let mut w = n as f64; // descending linear weights let mean = self.iter().map( |&x| { let lx = x as f64; let wx = w*lx; sx2 += wx*lx; w -= 1.; wx } ).sum::<f64>() as f64 / wsum(n); Ok( MStats { mean : mean, std : (sx2/wsum(n) - mean.powi(2)).sqrt() } ) } /// Harmonic mean of an i64 slice. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// assert_eq!(v1.hmean().unwrap(),4.305622526633627_f64); /// ``` fn hmean(&self) -> Result<f64> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"hmean - sample is empty!")); let mut sum = 0_f64; for &x in self { ensure!(x!=0_i64,emsg(file!(),line!(),"hmean does not accept zero valued data!")); sum += 1.0/(x as f64) } Ok ( n as f64 / sum ) } /// Linearly weighted harmonic mean of an i64 slice. /// Linearly descending weights from n down to one. /// Time dependent data should be in the stack order - the last being the oldest. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// assert_eq!(v1.hwmean().unwrap(),3.019546395306663_f64); /// ``` fn hwmean(&self) -> Result<f64> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"hwmean - sample is empty!")); let mut sum = 0_f64; let mut w = n as f64; for &x in self { ensure!(x!=0_i64,emsg(file!(),line!(),"hwmean does not accept zero valued data!")); sum += w/x as f64; w -= 1_f64; } Ok( wsum(n) / sum ) } /// Geometric mean of an i64 slice. /// The geometric mean is just an exponential of an arithmetic mean /// of log data (natural logarithms of the data items). /// The geometric mean is less sensitive to outliers near maximal value. /// Zero valued data is not allowed. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// assert_eq!(v1.gmean().unwrap(),6.045855171418503_f64); /// ``` fn gmean(&self) -> Result<f64> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"gmean - sample is empty!")); let mut sum = 0_f64; for &x in self { ensure!(x!=0_i64,emsg(file!(),line!(),"gmean does not accept zero valued data!")); sum += (x as f64).ln() } Ok( (sum/(n as f64)).exp() ) } /// Linearly weighted geometric mean of an i64 slice. /// Descending weights from n down to one. /// Time dependent data should be in the stack order - the last being the oldest. /// The geometric mean is just an exponential of an arithmetic mean /// of log data (natural logarithms of the data items). /// The geometric mean is less sensitive to outliers near maximal value. /// Zero data is not allowed - would at best only produce zero result. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// assert_eq!(v1.gwmean().unwrap(),4.144953510241978_f64); /// ``` fn gwmean(&self) -> Result<f64> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"gwmean - sample is empty!")); let mut w = n as f64; // descending weights let mut sum = 0_f64; for &x in self { ensure!(x!=0_i64,emsg(file!(),line!(),"gwmean does not accept zero valued data!")); sum += w*(x as f64).ln(); w -= 1_f64; } Ok( (sum/wsum(n)).exp() ) } /// Geometric mean and std ratio of an i64 slice. /// Zero valued data is not allowed. /// Std of ln data becomes a ratio after conversion back. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// let res = v1.gmeanstd().unwrap(); /// assert_eq!(res.mean,6.045855171418503_f64); /// assert_eq!(res.std,2.1084348239406303_f64); /// ``` fn gmeanstd(&self) -> Result<MStats> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"gmeanstd - sample is empty!")); let mut sum = 0_f64; let mut sx2 = 0_f64; for &x in self { ensure!(x!=0_i64,emsg(file!(),line!(),"gmeanstd does not accept zero valued data!")); let lx = (x as f64).ln(); sum += lx; sx2 += lx*lx } sum /= n as f64; Ok( MStats { mean: sum.exp(), std: (sx2/(n as f64) - sum.powi(2)).sqrt().exp() } ) } /// Linearly weighted version of gmeanstd. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// let res = v1.gwmeanstd().unwrap(); /// assert_eq!(res.mean,4.144953510241978_f64); /// assert_eq!(res.std,2.1572089236412597_f64); /// ``` fn gwmeanstd(&self) -> Result<MStats> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"gwmeanstd - sample is empty!")); let mut w = n as f64; // descending weights let mut sum = 0_f64; let mut sx2 = 0_f64; for &x in self { ensure!(x!=0_i64,emsg(file!(),line!(),"gwmeanstd does not accept zero valued data!")); let lnx = (x as f64).ln(); sum += w*lnx; sx2 += w*lnx*lnx; w -= 1_f64; } sum /= wsum(n); Ok( MStats { mean : sum.exp(), std : (sx2 as f64/wsum(n) - sum.powi(2)).sqrt().exp() } ) } /// Fast median (avoids sorting). /// The data values must be within a moderate range not exceeding u16size (65535). /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// let res = v1.median().unwrap(); /// assert_eq!(res.median,7.5_f64); /// assert_eq!(res.lquartile,4_f64); /// assert_eq!(res.uquartile,11_f64); /// ``` fn median(&self) -> Result<Med> { let n = self.len() as u32; ensure!(n>0,emsg(file!(),line!(),"median - sample is empty!")); ensure!(n<=u32::max_value(), emsg(file!(),line!(),"median - sample is probably too large!")); let max = *self.iter().max().with_context(||emsg(file!(),line!(),"median failed to find maximum"))?; let min = *self.iter().min().with_context(||emsg(file!(),line!(),"median failed to find minimum"))?; let range = (max-min+1) as usize; ensure!(range <= u32::max_value() as usize, // range is probably too large to use as subscripts "{}:{} rstats median range {} of values is too large",file!(),line!(),range); let mut acc = vec![0_u32; range]; // min max values inclusive for &item in self { acc[(item-min) as usize] += 1_u32 } // computes frequency distribution let mut result: Med = Default::default(); let mut cumm = 0_u32; let mut i2; for i in 0..range { // find the lower quartile cumm += acc[i]; // accummulate frequencies if 4 * cumm >= n { result.lquartile = (i as i64 + min) as f64; // restore min value break; } } cumm = 0u32; for i in (0..range).rev() { // find the upper quartile cumm += acc[i]; // accummulate frequencies if 4 * cumm >= n { result.uquartile = (i as i64 + min) as f64; break; } } cumm = 0u32; for i in 0..range { // find the midpoint of the frequency distribution cumm += acc[i]; // accummulate frequencies if 2 * cumm == n { // even, the other half must have the same value i2 = i + 1; while acc[i2] == 0 { i2 += 1 } // first next non-zero acc[i2] must represent the other half result.median = ((i + i2) as i64 + 2*min) as f64 / 2_f64; break; } if 2 * cumm > n { // first over the half items, this must be the odd midpoint result.median = (i as i64 + min) as f64; break; } } Ok(result) } /// Correlation coefficient of a sample of two integer variables. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// let v2 = vec![14_i64,13,12,11,10,9,8,7,6,5,4,3,2,1]; /// assert_eq!(v1.correlation(&v2).unwrap(),-1_f64); /// ``` fn correlation(&self,v2:&[i64]) -> Result<f64> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"correlation - first sample is empty")); ensure!(n==v2.len(),emsg(file!(),line!(),"correlation - samples are not of the same size")); let (mut sy,mut sxy,mut sx2,mut sy2) = (0_f64,0_f64,0_f64,0_f64); let sx:f64 = self.iter().enumerate().map(|(i,&vx)| { let x = vx as f64; let y = v2[i] as f64; sy += y; sxy += x*y; sx2 += x*x; sy2 += y*y; x }).sum(); let nf = n as f64; Ok( (sxy-sx/nf*sy)/(((sx2-sx/nf*sx)*(sy2-sy/nf*sy)).sqrt()) ) } /// Correlation coefficient of samples of i64 and f64 variables. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// let v2 = vec![14_f64,13.,12.,11.,10.,9.,8.,7.,6.,5.,4.,3.,2.,1.]; /// assert_eq!(v1.fcorrelation(&v2).unwrap(),-1_f64); /// ``` fn fcorrelation(&self,v2:&[f64]) -> Result<f64> { let n = self.len(); ensure!(n>0,emsg(file!(),line!(),"correlation - first sample is empty")); ensure!(n==v2.len(),emsg(file!(),line!(),"correlation - samples are not of the same size")); let (mut sy,mut sxy,mut sx2,mut sy2) = (0_f64,0_f64,0_f64,0_f64); let sx:f64 = self.iter().enumerate().map(|(i,&vx)| { let x = vx as f64; let y = v2[i]; sy += y; sxy += x*y; sx2 += x*x; sy2 += y*y; x }).sum(); let nf = n as f64; Ok( (sxy-sx/nf*sy)/(((sx2-sx/nf*sx)*(sy2-sy/nf*sy)).sqrt()) ) } /// (Auto)correlation coefficient of pairs of successive values of (time series) integer variable. /// # Example /// ``` /// use rstats::RStats; /// let v1 = vec![1_i64,2,3,4,5,6,7,8,9,10,11,12,13,14]; /// assert_eq!(v1.autocorr().unwrap(),0.9984603532054123_f64); /// ``` fn autocorr(&self) -> Result<f64> { let n = self.len(); ensure!(n>=2,emsg(file!(),line!(),"autocorr - sample is too small")); let (mut sx,mut sy,mut sxy,mut sx2,mut sy2) = (0_f64,0_f64,0_f64,0_f64,0_f64); for i in 0..n-1 { let x = self[i] as f64; let y = self[i+1] as f64; sx += x; sy += y; sxy += x*y; sx2 += x*x; sy2 += y*y } let nf = n as f64; Ok( (sxy-sx/nf*sy)/(((sx2-sx/nf*sx)*(sy2-sy/nf*sy)).sqrt()) ) } }