Performs linear regression model-based training in the the first step of the distributed processing mode.
More...
- Parameters
-
fptype | Data type to use in intermediate computations for linear regression model-based training, double or float |
method | Linear regression training method, Method |
- Enumerations
- Method Computation methods
- References
-
- Aliases
Distributed_Step1LocalFloat64QrDense
is an alias of Distributed(step=daal.step1Local, fptype=float64, method=daal.algorithms.linear_regression.training.qrDense)
Distributed_Step1LocalFloat64NormEqDense
is an alias of Distributed(step=daal.step1Local, fptype=float64, method=daal.algorithms.linear_regression.training.normEqDense)
Distributed_Step1LocalFloat32QrDense
is an alias of Distributed(step=daal.step1Local, fptype=float32, method=daal.algorithms.linear_regression.training.qrDense)
Distributed_Step1LocalFloat32NormEqDense
is an alias of Distributed(step=daal.step1Local, fptype=float32, method=daal.algorithms.linear_regression.training.normEqDense)
Distributed_Step2MasterFloat64QrDense
is an alias of Distributed(step=daal.step2Master, fptype=float64, method=daal.algorithms.linear_regression.training.qrDense)
Distributed_Step2MasterFloat64NormEqDense
is an alias of Distributed(step=daal.step2Master, fptype=float64, method=daal.algorithms.linear_regression.training.normEqDense)
Distributed_Step2MasterFloat32QrDense
is an alias of Distributed(step=daal.step2Master, fptype=float32, method=daal.algorithms.linear_regression.training.qrDense)
Distributed_Step2MasterFloat32NormEqDense
is an alias of Distributed(step=daal.step2Master, fptype=float32, method=daal.algorithms.linear_regression.training.normEqDense)
def __init__ |
( |
|
self, |
|
|
|
args |
|
) |
| |
- Variant 1
- Default constructor
- Variant 2
Constructs a linear regression training algorithm in the first step of the distributed processing mode by copying input objects and parameters of another linear regression training algorithm
- Parameters
-
other | Algorithm to use as the source to initialize the input objects and parameters of the algorithm |
Returns a pointer to a newly allocated linear regression training algorithm with a copy of the input objects and parameters of this linear regression training algorithm in the first step of the distributed processing mode
- Returns
- Pointer to the newly allocated algorithm
Invokes computations and returns partial result
def finalizeCompute |
( |
|
self | ) |
|
Finalizes computations and returns (final) result
The documentation for this class was generated from the following file:
- linear_regression/training.py