TensorOperator: R6 singleton that contains the configuration for the tensor...
In BKTR: Bayesian Kernelized Tensor Regression
TensorOperator R Documentation
R6 singleton that contains the configuration for the tensor backend
Description
Tensor backend configuration and methods for all the tensor operations
in BKTR
Super class
R6P::Singleton -> TensorOperator
Public fields
fp_typeThe floating point type to use for the tensor operations
fp_deviceThe device to use for the tensor operations
Methods
Public methods
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Method new()
Initialize the tensor operator with the given floating point type
and device
Usage
TensorOperator$new(fp_type = "float64", fp_device = "cpu")
Arguments
fp_typeThe floating point type to use for the tensor operations (either
"float64" or "float32")
fp_deviceThe device to use for the tensor operations (either "cpu" or
"cuda")
Returns
A new tensor operator instance
Method set_params()
Set the tensor operator parameters
Usage
TensorOperator$set_params(fp_type = NULL, fp_device = NULL, seed = NULL)
Arguments
fp_typeThe floating point type to use for the tensor operations (either
"float64" or "float32")
fp_deviceThe device to use for the tensor operations (either "cpu" or
"cuda")
seedThe seed to use for the random number generator
Method get_default_jitter()
Get the default jitter value for the floating point type used by the tensor operator
Usage
TensorOperator$get_default_jitter()
Returns
The default jitter value for the floating point type used by the tensor operator
Method tensor()
Create a tensor from a vector or matrix of data with the tensor operator dtype and device
Usage
TensorOperator$tensor(tensor_data)
Arguments
tensor_dataThe vector or matrix of data to create the tensor from
Returns
A new tensor with the tensor operator dtype and device
Method is_tensor()
Check if a provided object is a tensor
Usage
TensorOperator$is_tensor(tensor)
Arguments
tensorThe object to check
Returns
A boolean indicating if the object is a tensor
Method eye()
Create a tensor with a diagonal of ones and zeros with the tensor operator dtype and device
for a given dimension
Usage
TensorOperator$eye(eye_dim)
Arguments
eye_dimThe dimension of the tensor to create
Returns
A new tensor with a diagonal of ones and zeros with the tensor operator dtype and device
Method ones()
Create a tensor of ones with the tensor operator dtype and device for a given dimension
Usage
TensorOperator$ones(tsr_dim)
Arguments
tsr_dimThe dimension of the tensor to create
Returns
A new tensor of ones with the tensor operator dtype and device
Method zeros()
Create a tensor of zeros with the tensor operator dtype and device for a given dimension
Usage
TensorOperator$zeros(tsr_dim)
Arguments
tsr_dimThe dimension of the tensor to create
Returns
A new tensor of zeros with the tensor operator dtype and device
Method rand()
Create a tensor of random uniform values with the tensor operator dtype and
device for a given dimension
Usage
TensorOperator$rand(tsr_dim)
Arguments
tsr_dimThe dimension of the tensor to create
Returns
A new tensor of random values with the tensor operator dtype and device
Method randn()
Create a tensor of random normal values with the tensor operator dtype and device
for a given dimension
Usage
TensorOperator$randn(tsr_dim)
Arguments
tsr_dimThe dimension of the tensor to create
Returns
A new tensor of random normal values with the tensor operator dtype and device
Method randn_like()
Create a tensor of random uniform values with the same shape as a given tensor
with the tensor operator dtype and device
Usage
TensorOperator$randn_like(input_tensor)
Arguments
input_tensorThe tensor to use as a shape reference
Returns
A new tensor of random uniform values with the same shape as a given tensor
Method arange()
Create a tensor of a range of values with the tensor operator dtype and device
for a given start and end
Usage
TensorOperator$arange(start, end)
Arguments
startThe start of the range
endThe end of the range
Returns
A new tensor of a range of values with the tensor operator dtype and device
Method rand_choice()
Choose random values from a tensor for a given number of samples
Usage
TensorOperator$rand_choice(
choices_tsr,
nb_sample,
use_replace = FALSE,
weights_tsr = NULL
)
Arguments
choices_tsrThe tensor to choose values from
nb_sampleThe number of samples to choose
use_replaceA boolean indicating if the sampling should be done with replacement.
Defaults to FALSE
weights_tsrThe weights to use for the sampling. If NULL, the sampling is uniform.
Defaults to NULL
Returns
A new tensor of randomly chosen values from a tensor
Method kronecker_prod()
Efficiently compute the kronecker product of two matrices in tensor format
Usage
TensorOperator$kronecker_prod(a, b)
Arguments
aThe first tensor
bThe second tensor
Returns
The kronecker product of the two matrices
Method khatri_rao_prod()
Efficiently compute the khatri rao product of two matrices in tensor format
having the same number of columns
Usage
TensorOperator$khatri_rao_prod(a, b)
Arguments
aThe first tensor
bThe second tensor
Returns
The khatri rao product of the two matrices
Method clone()
The objects of this class are cloneable with this method.
Usage
TensorOperator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
# Set the seed, setup the tensor floating point type and device
TSR$set_params(fp_type='float64', fp_device='cpu', seed=42)
# Create a tensor from a vector
TSR$tensor(c(1, 2, 3))
# Create a tensor from a matrix
TSR$tensor(matrix(c(1, 2, 3, 4), nrow=2))
# Create a 3x3 tensor with a diagonal of ones and zeros elsewhere
TSR$eye(3)
# Create a tensor of ones (with 6 elements, 2 rows and 3 columns)
TSR$ones(c(2, 3))
# Create a tensor of zeros (with 12 elements, 3 rows and 4 columns)
TSR$zeros(c(3, 4))
# Create a tensor of random uniform values (with 6 elements)
TSR$rand(c(2, 3))
# Create a tensor of random normal values (with 6 elements)
TSR$randn(c(2, 3))
# Create a tensor of random normal values with the same shape as a given tensor
tsr_a <- TSR$randn(c(2, 3))
TSR$randn_like(tsr_a)
# Create a tensor of a range of values (1, 2, 3, 4)
TSR$arange(1, 4)
# Choose two random values from a given tensor without replacement
tsr_b <- TSR$rand(6)
TSR$rand_choice(tsr_b, 2)
# Use the tensor operator to compute the kronecker product of two 2x2 matrices
tsr_c <- TSR$tensor(matrix(c(1, 2, 3, 4), nrow=2))
tsr_d <- TSR$tensor(matrix(c(5, 6, 7, 8), nrow=2))
TSR$kronecker_prod(tsr_c, tsr_d) # Returns a 4x4 tensor
# Use the tensor operator to compute the khatri rao product of two 2x2 matrices
TSR$khatri_rao_prod(tsr_c, tsr_d) # Returns a 4x2 tensor
# Check if a given object is a tensor
TSR$is_tensor(tsr_d) # Returns TRUE
TSR$is_tensor(TSR$eye(2)) # Returns TRUE
TSR$is_tensor(1) # Returns FALSE
BKTR documentation built on Sept. 12, 2024, 7:04 a.m.
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| TensorOperator | R Documentation |
R6 singleton that contains the configuration for the tensor backend
Description
Tensor backend configuration and methods for all the tensor operations in BKTR
Super class
R6P::Singleton -> TensorOperator
Public fields
fp_typeThe floating point type to use for the tensor operations
fp_deviceThe device to use for the tensor operations
Methods
Public methods
Method new()
Initialize the tensor operator with the given floating point type and device
Usage
TensorOperator$new(fp_type = "float64", fp_device = "cpu")
Arguments
fp_typeThe floating point type to use for the tensor operations (either "float64" or "float32")
fp_deviceThe device to use for the tensor operations (either "cpu" or "cuda")
Returns
A new tensor operator instance
Method set_params()
Set the tensor operator parameters
Usage
TensorOperator$set_params(fp_type = NULL, fp_device = NULL, seed = NULL)
Arguments
fp_typeThe floating point type to use for the tensor operations (either "float64" or "float32")
fp_deviceThe device to use for the tensor operations (either "cpu" or "cuda")
seedThe seed to use for the random number generator
Method get_default_jitter()
Get the default jitter value for the floating point type used by the tensor operator
Usage
TensorOperator$get_default_jitter()
Returns
The default jitter value for the floating point type used by the tensor operator
Method tensor()
Create a tensor from a vector or matrix of data with the tensor operator dtype and device
Usage
TensorOperator$tensor(tensor_data)
Arguments
tensor_dataThe vector or matrix of data to create the tensor from
Returns
A new tensor with the tensor operator dtype and device
Method is_tensor()
Check if a provided object is a tensor
Usage
TensorOperator$is_tensor(tensor)
Arguments
tensorThe object to check
Returns
A boolean indicating if the object is a tensor
Method eye()
Create a tensor with a diagonal of ones and zeros with the tensor operator dtype and device for a given dimension
Usage
TensorOperator$eye(eye_dim)
Arguments
eye_dimThe dimension of the tensor to create
Returns
A new tensor with a diagonal of ones and zeros with the tensor operator dtype and device
Method ones()
Create a tensor of ones with the tensor operator dtype and device for a given dimension
Usage
TensorOperator$ones(tsr_dim)
Arguments
tsr_dimThe dimension of the tensor to create
Returns
A new tensor of ones with the tensor operator dtype and device
Method zeros()
Create a tensor of zeros with the tensor operator dtype and device for a given dimension
Usage
TensorOperator$zeros(tsr_dim)
Arguments
tsr_dimThe dimension of the tensor to create
Returns
A new tensor of zeros with the tensor operator dtype and device
Method rand()
Create a tensor of random uniform values with the tensor operator dtype and device for a given dimension
Usage
TensorOperator$rand(tsr_dim)
Arguments
tsr_dimThe dimension of the tensor to create
Returns
A new tensor of random values with the tensor operator dtype and device
Method randn()
Create a tensor of random normal values with the tensor operator dtype and device for a given dimension
Usage
TensorOperator$randn(tsr_dim)
Arguments
tsr_dimThe dimension of the tensor to create
Returns
A new tensor of random normal values with the tensor operator dtype and device
Method randn_like()
Create a tensor of random uniform values with the same shape as a given tensor with the tensor operator dtype and device
Usage
TensorOperator$randn_like(input_tensor)
Arguments
input_tensorThe tensor to use as a shape reference
Returns
A new tensor of random uniform values with the same shape as a given tensor
Method arange()
Create a tensor of a range of values with the tensor operator dtype and device for a given start and end
Usage
TensorOperator$arange(start, end)
Arguments
startThe start of the range
endThe end of the range
Returns
A new tensor of a range of values with the tensor operator dtype and device
Method rand_choice()
Choose random values from a tensor for a given number of samples
Usage
TensorOperator$rand_choice( choices_tsr, nb_sample, use_replace = FALSE, weights_tsr = NULL )
Arguments
choices_tsrThe tensor to choose values from
nb_sampleThe number of samples to choose
use_replaceA boolean indicating if the sampling should be done with replacement. Defaults to FALSE
weights_tsrThe weights to use for the sampling. If NULL, the sampling is uniform. Defaults to NULL
Returns
A new tensor of randomly chosen values from a tensor
Method kronecker_prod()
Efficiently compute the kronecker product of two matrices in tensor format
Usage
TensorOperator$kronecker_prod(a, b)
Arguments
aThe first tensor
bThe second tensor
Returns
The kronecker product of the two matrices
Method khatri_rao_prod()
Efficiently compute the khatri rao product of two matrices in tensor format having the same number of columns
Usage
TensorOperator$khatri_rao_prod(a, b)
Arguments
aThe first tensor
bThe second tensor
Returns
The khatri rao product of the two matrices
Method clone()
The objects of this class are cloneable with this method.
Usage
TensorOperator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
# Set the seed, setup the tensor floating point type and device
TSR$set_params(fp_type='float64', fp_device='cpu', seed=42)
# Create a tensor from a vector
TSR$tensor(c(1, 2, 3))
# Create a tensor from a matrix
TSR$tensor(matrix(c(1, 2, 3, 4), nrow=2))
# Create a 3x3 tensor with a diagonal of ones and zeros elsewhere
TSR$eye(3)
# Create a tensor of ones (with 6 elements, 2 rows and 3 columns)
TSR$ones(c(2, 3))
# Create a tensor of zeros (with 12 elements, 3 rows and 4 columns)
TSR$zeros(c(3, 4))
# Create a tensor of random uniform values (with 6 elements)
TSR$rand(c(2, 3))
# Create a tensor of random normal values (with 6 elements)
TSR$randn(c(2, 3))
# Create a tensor of random normal values with the same shape as a given tensor
tsr_a <- TSR$randn(c(2, 3))
TSR$randn_like(tsr_a)
# Create a tensor of a range of values (1, 2, 3, 4)
TSR$arange(1, 4)
# Choose two random values from a given tensor without replacement
tsr_b <- TSR$rand(6)
TSR$rand_choice(tsr_b, 2)
# Use the tensor operator to compute the kronecker product of two 2x2 matrices
tsr_c <- TSR$tensor(matrix(c(1, 2, 3, 4), nrow=2))
tsr_d <- TSR$tensor(matrix(c(5, 6, 7, 8), nrow=2))
TSR$kronecker_prod(tsr_c, tsr_d) # Returns a 4x4 tensor
# Use the tensor operator to compute the khatri rao product of two 2x2 matrices
TSR$khatri_rao_prod(tsr_c, tsr_d) # Returns a 4x2 tensor
# Check if a given object is a tensor
TSR$is_tensor(tsr_d) # Returns TRUE
TSR$is_tensor(TSR$eye(2)) # Returns TRUE
TSR$is_tensor(1) # Returns FALSE
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