Given a list of floating point numbers, standardize it.

###Details

• A list \$x_1,x_2,\ldots,x_n\$ is standardized if the mean of all values is 0, and the standard deviation is 1. One way to compute this is by first computing the mean \$\mu\$ and the standard deviation \$\sigma\$ as $$ \mu = \frac1n\sum_{i=1}^n x_i \qquad \sigma = \sqrt{\frac{1}{n}\sum_{i=1}^n (x_i -\mu)^2} ,$$ and then computing the standardization by replacing every \$x_i\$ with \$\frac{x_i-\mu}{\sigma}\$.
• You can assume that the input contains at least two distinct entries (which implies \$\sigma \neq 0\$).
• Note that some implementations use the sample standard deviation, which is not equal to the population standard deviation \$\sigma\$ we are using here.
• There is a CW answer for all trivial solutions.

###Examples

[1,2,3] -> [-1.224744871391589,0.0,1.224744871391589]
[1,2] -> [-1,1]
[-3,1,4,1,5] -> [-1.6428571428571428,-0.21428571428571433,0.8571428571428572,-0.21428571428571433,1.2142857142857144]

(These examples have been generated with this script.)

Given a list of floating point numbers, standardize it.

Details

• A list \$x_1,x_2,\ldots,x_n\$ is standardized if the mean of all values is 0, and the standard deviation is 1. One way to compute this is by first computing the mean \$\mu\$ and the standard deviation \$\sigma\$ as $$ \mu = \frac1n\sum_{i=1}^n x_i \qquad \sigma = \sqrt{\frac{1}{n}\sum_{i=1}^n (x_i -\mu)^2} ,$$ and then computing the standardization by replacing every \$x_i\$ with \$\frac{x_i-\mu}{\sigma}\$.
• You can assume that the input contains at least two distinct entries (which implies \$\sigma \neq 0\$).
• Note that some implementations use the sample standard deviation, which is not equal to the population standard deviation \$\sigma\$ we are using here.
• There is a CW answer for all trivial solutions.

Examples

[1,2,3] -> [-1.224744871391589,0.0,1.224744871391589]
[1,2] -> [-1,1]
[-3,1,4,1,5] -> [-1.6428571428571428,-0.21428571428571433,0.8571428571428572,-0.21428571428571433,1.2142857142857144]

(These examples have been generated with this script.)

Given a list of floating point numbers, standardize it.

###Details

• A list \$x_1,x_2,\ldots,x_n\$ is standardized if the mean of all values is 0, and the standard deviation is 1. One way to compute this is by first computing the mean \$\mu\$ and the standard deviation \$\sigma\$ as $$ \mu = \frac1n\sum_{i=1}^n x_i \qquad \sigma = \sqrt{\frac{1}{n}\sum_{i=1}^n (x_i -\mu)^2} ,$$ and then computing the standardization by replacing every \$x_i\$ with \$\frac{x_i-\mu}{\sigma}\$.
• You can assume that the input contains at least two distinct entries (which implies \$\sigma \neq 0\$).

###Examples

[1,2,3] -> [-1.224744871391589,0.0,1.224744871391589]
[1,2] -> [-1,1]
[-3,1,4,1,5] -> [-1.6428571428571428,-0.21428571428571433,0.8571428571428572,-0.21428571428571433,1.2142857142857144]

(These examples have been generated with this script.)

Given a list of floating point numbers, standardize it.

###Details

• A list \$x_1,x_2,\ldots,x_n\$ is standardized if the mean of all values is 0, and the standard deviation is 1. One way to compute this is by first computing the mean \$\mu\$ and the standard deviation \$\sigma\$ as $$ \mu = \frac1n\sum_{i=1}^n x_i \qquad \sigma = \sqrt{\frac{1}{n}\sum_{i=1}^n (x_i -\mu)^2} ,$$ and then computing the standardization by replacing every \$x_i\$ with \$\frac{x_i-\mu}{\sigma}\$.
• You can assume that the input contains at least two distinct entries (which implies \$\sigma \neq 0\$).
• Note that some implementations use the sample standard deviation, which is not equal to the population standard deviation \$\sigma\$ we are using here.
• There is a CW answer for all trivial solutions .

###Examples

[1,2,3] -> [-1.224744871391589,0.0,1.224744871391589]
[1,2] -> [-1,1]
[-3,1,4,1,5] -> [-1.6428571428571428,-0.21428571428571433,0.8571428571428572,-0.21428571428571433,1.2142857142857144]

(These examples have been generated with this script.)