pnorm

Z-score 标准化(zero-mean normalization)

也叫标准差标准化，经过处理的数据符合标准正态分布，即均值为0，标准差为1 其中μ为所有样本数据的均值，σ为所有样本数据的标准差。

注：是否要进行标准化，要根据具体实验定。如果特征非常稀疏，并且有大量的0（现实应用中很多特征都具有这个特点）， Z-score 标准化的过程几乎就是一个除0的过程，结果不可预料。

Standard score(z-score)

In statistics, the standard score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured. Observed values above the mean have positive standard scores, while values below the mean have negative standard scores. The standard score is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios; see normalization for more).

https://en.wikipedia.org/wiki/Standard_score

http://blog.163.com/huai_jing@126/blog/static/171861983201321074124426/

Standard score(z-score)

In statistics, the standard score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured. Observed values above the mean have positive standard scores, while values below the mean have negative standard scores. The standard score is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios; see normalization for more).

https://en.wikipedia.org/wiki/Standard_score

http://blog.163.com/huai_jing@126/blog/static/171861983201321074124426/

A logistic function or logistic curve is a common "S" shape (sigmoid curve)

https://en.wikipedia.org/wiki/Logistic_function

0-1标准化(0-1 normalization)

也叫离差标准化，是对原始数据的线性变换，使结果落到[0,1]区间 其中max为样本数据的最大值，min为样本数据的最小值。这种方法有一个缺陷就是当有新数据加入时，可能导致max和min的变化，需要重新定义。

数据的标准化（normalization）是将数据按比例缩放，使之落入一个小的特定区间。这样去除数据的单位限制， 将其转化为无量纲的纯数值，便于不同单位或量级的指标能够进行比较和加权。 其中最典型的就是0-1标准化和Z标准化

https://github.com/mpadge/tnorm

标准正态分布, delta = 1, u = 0

Density, distribution function, quantile function and random generation for the normal distribution with mean equal to mean and standard deviation equal to sd.

implements of the R language pnorm function

Density, distribution function, quantile function and random generation for the normal distribution with mean equal to mean and standard deviation equal to sd.

implements of the R language pnorm function

normal-pdf

Gaussian PDF function, Normal Distribution.

(正态分布)

Get normal distribution density value at a point.