computeCost: computeCost
In GuangchuangYu/mlass: Machine Learning Algorithms
Description
Usage
Arguments
Details
Value
Author(s)
Description
computing the cost J
Usage
1
computeCost(X, y, theta, lambda = 0)
Arguments
X
x values (a column of 1 was added)
y
y values
theta
theta values
Details
The gradient descent algorithm was to minimize the cost function.
Value
cost J
Author(s)
Guangchuang Yu http://ygc.name
GuangchuangYu/mlass documentation built on May 6, 2019, 9:02 p.m.
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Description Usage Arguments Details Value Author(s)
Description
computing the cost J
Usage
1 | computeCost(X, y, theta, lambda = 0)
|
Arguments
X |
x values (a column of 1 was added) |
y |
y values |
theta |
theta values |
Details
The gradient descent algorithm was to minimize the cost function.
Value
cost J
Author(s)
Guangchuang Yu http://ygc.name
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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