softmaxreg: Softmax regression with gradient descent
In damingli09/bis557: Package for homework of BIS557 Computational Statistics
Description
Fits softmax regression using gradient descent, by maximizing log-likelihood
Usage
1
softmaxreg(X, y, lrate = 0.01, maxiter = 10000, tol = 1e-05)
Arguments
X
the design matrix
y
the response variable
lrate
the learning rate
maxiter
the maximum iteration number
tol
the numerical tolerance
damingli09/bis557 documentation built on Nov. 21, 2020, 9:11 a.m.
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Description
Fits softmax regression using gradient descent, by maximizing log-likelihood
Usage
1 | softmaxreg(X, y, lrate = 0.01, maxiter = 10000, tol = 1e-05)
|
Arguments
X |
the design matrix |
y |
the response variable |
lrate |
the learning rate |
maxiter |
the maximum iteration number |
tol |
the numerical tolerance |
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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