Numpy 模块

小技巧

请使用 from heu import numpy as hnp 引入 phe 模块

a numpy adapter for phe module which provides numpy-like api

class heu.numpy.CiphertextArray

property cols

Get the number of cols

property ndim

The array’s number of dimensions

property rows

Get the number of rows

property shape

The array’s shape

property size

Number of elements in the array

transpose(self: heu.numpy.CiphertextArray) → heu.numpy.CiphertextArray

Transpose the array

class heu.numpy.Decryptor

decrypt(*args, **kwargs)

Overloaded function.

1. decrypt(self: heu.numpy.Decryptor, ciphertext: heu.phe.Ciphertext) -> heu.phe.Plaintext

Decrypt ciphertext (scalar) to plaintext (scalar)

2. decrypt(self: heu.numpy.Decryptor, ciphertext_array: heu.numpy.CiphertextArray) -> heu.numpy.PlaintextArray

Decrypt ciphertext array to plaintext array

property phe

class heu.numpy.DestinationHeKit

array(*args, **kwargs)

Overloaded function.

1. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.BigintEncoderParams = BigintEncoderParams()) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBigintEncoderParams

2. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.BigintEncoderParams = BigintEncoderParams()) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBigintEncoderParams

3. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.BigintEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBigintEncoder, same with hnp.array()

4. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.BigintEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBigintEncoder, same with hnp.array()

5. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.IntegerEncoderParams) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyIntegerEncoderParams

6. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.IntegerEncoderParams) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyIntegerEncoderParams

7. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.IntegerEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyIntegerEncoder, same with hnp.array()

8. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.IntegerEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyIntegerEncoder, same with hnp.array()

9. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.FloatEncoderParams) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyFloatEncoderParams

10. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.FloatEncoderParams) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyFloatEncoderParams

11. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.FloatEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyFloatEncoder, same with hnp.array()

12. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.FloatEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyFloatEncoder, same with hnp.array()

13. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.BatchIntegerEncoderParams) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchIntegerEncoderParams

14. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.BatchIntegerEncoderParams) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchIntegerEncoderParams

15. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.BatchIntegerEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchIntegerEncoder, same with hnp.array()

16. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.BatchIntegerEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchIntegerEncoder, same with hnp.array()

17. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.BatchFloatEncoderParams) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchFloatEncoderParams

18. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.BatchFloatEncoderParams) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchFloatEncoderParams

19. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.BatchFloatEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchFloatEncoder, same with hnp.array()

20. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.BatchFloatEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchFloatEncoder, same with hnp.array()

encryptor(self: heu.numpy.DestinationHeKit) → heu::lib::numpy::Encryptor

Get encryptor

evaluator(self: heu.numpy.DestinationHeKit) → heu::lib::numpy::Evaluator

Get evaluator

class heu.numpy.Encryptor

encrypt(*args, **kwargs)

Overloaded function.

1. encrypt(self: heu.numpy.Encryptor, plaintext: heu.phe.Plaintext) -> heu.phe.Ciphertext

Encrypt plaintext (scalar) to ciphertext (scalar)

2. encrypt(self: heu.numpy.Encryptor, plaintext_array: heu.numpy.PlaintextArray) -> heu.numpy.CiphertextArray

Encrypt plaintext array to ciphertext array

encrypt_with_audit(self: heu.numpy.Encryptor, arg0: heu.numpy.PlaintextArray) → Tuple[heu.numpy.CiphertextArray, heu.numpy.StringArray]

Encrypt and build audit string including plaintext/random/ciphertext info

property phe

Get the instance of phe.Encryptor for scalar encryption

class heu.numpy.Evaluator

add(*args, **kwargs)

Overloaded function.

1. add(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: heu.numpy.CiphertextArray) -> heu.numpy.CiphertextArray

2. add(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: heu.numpy.PlaintextArray) -> heu.numpy.CiphertextArray

3. add(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: heu.numpy.CiphertextArray) -> heu.numpy.CiphertextArray

4. add(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: heu.numpy.PlaintextArray) -> heu.numpy.PlaintextArray

batch_feature_wise_bucket_sum(*args, **kwargs)

Overloaded function.

1. batch_feature_wise_bucket_sum(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: List[numpy.ndarray[numpy.int8[1, n]]], arg2: numpy.ndarray[numpy.int8[m, n], flags.c_contiguous], arg3: int, arg4: bool) -> List[heu.numpy.PlaintextArray]

Take elements in x according to order_map to caculate the row sum at each bin for each subgroup (Plaintext) e heu numpy evaluator x dense matrix, rows are elements of bin sum subgroup_map a list of 1d py np ndarray (vector) acts as filters. Its length should be equal to x.rows(), elements should be 0 or 1, with 1 indicates in this subgroup. order_map a 2d py ndarray. It has shape x.rows() * number of features.order_map(i, j) = k means row i feature j should be in bin k of feature j. bucket_num int. The number of buckets for each bin. cumsum bool. If apply cumulative sum to buckets for each feature. return list of dense matrix<T>, the row bin sum results. Each element has shape (bucket_num * feature_num, x.cols()).

2. batch_feature_wise_bucket_sum(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: List[numpy.ndarray[numpy.int8[1, n]]], arg2: numpy.ndarray[numpy.int8[m, n], flags.c_contiguous], arg3: int, arg4: bool) -> List[heu.numpy.CiphertextArray]

Take elements in x according to order_map to caculate the row sum at each bin for each subgroup (Ciphertext) e heu numpy evaluator x dense matrix, rows are elements of bin sum subgroup_map a list of 1d py np ndarray (vector) acts as filters. Its length should be equal to x.rows(), elements should be 0 or 1, with 1 indicates in this subgroup. order_map a 2d py ndarray. It has shape x.rows() * number of features.order_map(i, j) = k means row i feature j should be in bin k of feature j. bucket_num int. The number of buckets for each bin. cumsum bool. If apply cumulative sum to buckets for each feature. return list of dense matrix<T>, the row bin sum results. Each element has shape (bucket_num * feature_num, x.cols()).

batch_select_sum(*args, **kwargs)

Overloaded function.

1. batch_select_sum(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: List[object]) -> heu.numpy.PlaintextArray

Compute an array of sum of selected elements (Plaintext), equivalent to [x[indices].sum() for indices in indices_list] but faster.

2. batch_select_sum(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: List[object]) -> heu.numpy.CiphertextArray

Compute an array of sum of selected elements (Ciphertext), equivalent to [x[indices].sum() for indices in indices_list] but faster.

feature_wise_bucket_sum(*args, **kwargs)

Overloaded function.

1. feature_wise_bucket_sum(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: numpy.ndarray[numpy.int8[1, n]], arg2: numpy.ndarray[numpy.int8[m, n], flags.c_contiguous], arg3: int, arg4: bool) -> heu.numpy.PlaintextArray

Take elements in x according to order_map to caculate the row sum at each bin. (Plaintext) e heu numpy evaluator x dense matrix, rows are elements of bin sum subgroup_map a 1d py np ndarray (vector) acts as a filter. Its length should be equal to x.rows(), elements should be 0 or 1, with 1 indicates in this subgroup. order_map a 2d py ndarray. It has shape x.rows() * number of features.order_map(i, j) = k means row i feature j should be in bin k of feature j. bucket_num int. The number of buckets for each bin. cumsum bool. If apply cumulative sum to buckets for each feature. return dense matrix<T>, the row bin sum result. It has shape (bucket_num * feature_num, x.cols()).

2. feature_wise_bucket_sum(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: numpy.ndarray[numpy.int8[1, n]], arg2: numpy.ndarray[numpy.int8[m, n], flags.c_contiguous], arg3: int, arg4: bool) -> heu.numpy.CiphertextArray

Take elements in x according to order_map to caculate the row sum at each bin. (Ciphertext) e heu numpy evaluator x dense matrix, rows are elements of bin sum subgroup_map a 1d py np ndarray (vector) acts as a filter. Its length should be equal to x.rows(), elements should be 0 or 1, with 1 indicates in this subgroup. order_map a 2d py ndarray. It has shape x.rows() * number of features.order_map(i, j) = k means row i feature j should be in bin k of feature j. bucket_num int. The number of buckets for each bin. cumsum bool. If apply cumulative sum to buckets for each feature. return dense matrix<T>, the row bin sum result. It has shape (bucket_num * feature_num, x.cols()).

matmul(*args, **kwargs)

Overloaded function.

1. matmul(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: heu.numpy.PlaintextArray) -> heu.numpy.PlaintextArray

2. matmul(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: heu.numpy.CiphertextArray) -> heu.numpy.CiphertextArray

3. matmul(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: heu.numpy.PlaintextArray) -> heu.numpy.CiphertextArray

mul(*args, **kwargs)

Overloaded function.

1. mul(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: heu.numpy.PlaintextArray) -> heu.numpy.CiphertextArray

2. mul(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: heu.numpy.CiphertextArray) -> heu.numpy.CiphertextArray

3. mul(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: heu.numpy.PlaintextArray) -> heu.numpy.PlaintextArray

property phe

select_sum(*args, **kwargs)

Overloaded function.

1. select_sum(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: object) -> heu.phe.Plaintext

Compute the sum of selected elements (Plaintext), equivalent to x[indices].sum() but faster.

2. select_sum(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: object) -> heu.phe.Ciphertext

Compute the sum of selected elements (Ciphertext), equivalent to x[indices].sum() but faster.

sub(*args, **kwargs)

Overloaded function.

1. sub(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: heu.numpy.CiphertextArray) -> heu.numpy.CiphertextArray

2. sub(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray, arg1: heu.numpy.PlaintextArray) -> heu.numpy.CiphertextArray

3. sub(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: heu.numpy.CiphertextArray) -> heu.numpy.CiphertextArray

4. sub(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray, arg1: heu.numpy.PlaintextArray) -> heu.numpy.PlaintextArray

sum(*args, **kwargs)

Overloaded function.

1. sum(self: heu.numpy.Evaluator, arg0: heu.numpy.PlaintextArray) -> heu.phe.Plaintext

2. sum(self: heu.numpy.Evaluator, arg0: heu.numpy.CiphertextArray) -> heu.phe.Ciphertext

class heu.numpy.HeKit

array(*args, **kwargs)

Overloaded function.

1. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.BigintEncoderParams = BigintEncoderParams()) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBigintEncoderParams

2. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.BigintEncoderParams = BigintEncoderParams()) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBigintEncoderParams

3. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.BigintEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBigintEncoder, same with hnp.array()

4. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.BigintEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBigintEncoder, same with hnp.array()

5. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.IntegerEncoderParams) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyIntegerEncoderParams

6. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.IntegerEncoderParams) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyIntegerEncoderParams

7. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.IntegerEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyIntegerEncoder, same with hnp.array()

8. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.IntegerEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyIntegerEncoder, same with hnp.array()

9. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.FloatEncoderParams) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyFloatEncoderParams

10. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.FloatEncoderParams) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyFloatEncoderParams

11. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.FloatEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyFloatEncoder, same with hnp.array()

12. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.FloatEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyFloatEncoder, same with hnp.array()

13. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.BatchIntegerEncoderParams) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchIntegerEncoderParams

14. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.BatchIntegerEncoderParams) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchIntegerEncoderParams

15. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.BatchIntegerEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchIntegerEncoder, same with hnp.array()

16. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.BatchIntegerEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchIntegerEncoder, same with hnp.array()

17. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder_params: heu.phe.BatchFloatEncoderParams) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchFloatEncoderParams

18. array(self: heu.phe.HeKitPublicBase, object: object, encoder_params: heu.phe.BatchFloatEncoderParams) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchFloatEncoderParams

19. array(self: heu.phe.HeKitPublicBase, ndarray: numpy.ndarray, encoder: heu.phe.BatchFloatEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchFloatEncoder, same with hnp.array()

20. array(self: heu.phe.HeKitPublicBase, object: object, encoder: heu.phe.BatchFloatEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchFloatEncoder, same with hnp.array()

decryptor(self: heu.numpy.HeKit) → heu::lib::numpy::Decryptor

Get decryptor

encryptor(self: heu.numpy.HeKit) → heu::lib::numpy::Encryptor

Get encryptor

evaluator(self: heu.numpy.HeKit) → heu::lib::numpy::Evaluator

Get evaluator

class heu.numpy.PlaintextArray

property cols

Get the number of cols

property ndim

The array’s number of dimensions

property rows

Get the number of rows

property shape

The array’s shape

property size

Number of elements in the array

to_bytes(self: heu.numpy.PlaintextArray, bytes_per_int: int, endian: str) → bytes

Construct Python bytes containing the raw data bytes in the array.

The endian argument determines the byte order used to represent an integer. To request the native byte order of the host system, use sys.byteorder as the byte order value.

to_numpy(*args, **kwargs)

Overloaded function.

1. to_numpy(self: heu.numpy.PlaintextArray, encoder: heu.phe.BigintDecoder = BigintDecoder()) -> numpy.ndarray

Decode plaintext array to numpy ndarray with type ‘object’

2. to_numpy(self: heu.numpy.PlaintextArray, encoder: heu.phe.IntegerEncoder) -> numpy.ndarray

Decode plaintext array to numpy ndarray with type ‘int64’

3. to_numpy(self: heu.numpy.PlaintextArray, encoder: heu.phe.FloatEncoder) -> numpy.ndarray

Decode plaintext array to numpy ndarray with type ‘float64’

4. to_numpy(self: heu.numpy.PlaintextArray, encoder: heu.phe.BatchIntegerEncoder) -> numpy.ndarray

Decode plaintext array to numpy ndarray with type ‘int64’

5. to_numpy(self: heu.numpy.PlaintextArray, encoder: heu.phe.BatchFloatEncoder) -> numpy.ndarray

Decode plaintext array to numpy ndarray with type ‘float64’

transpose(self: heu.numpy.PlaintextArray) → heu.numpy.PlaintextArray

Transpose the array

class heu.numpy.Shape

class heu.numpy.StringArray

property cols

Get the number of cols

property ndim

The array’s number of dimensions

property rows

Get the number of rows

property shape

The array’s shape

property size

Number of elements in the array

transpose(self: heu.numpy.StringArray) → heu.numpy.StringArray

Transpose the array

heu.numpy.array(*args, **kwargs)

Overloaded function.

1. array(ndarray: numpy.ndarray, encoder: heu.phe.BigintEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBigintEncoder

2. array(object: object, encoder: heu.phe.BigintEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBigintEncoder

3. array(ndarray: numpy.ndarray, encoder: heu.phe.IntegerEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyIntegerEncoder

4. array(object: object, encoder: heu.phe.IntegerEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyIntegerEncoder

5. array(ndarray: numpy.ndarray, encoder: heu.phe.FloatEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyFloatEncoder

6. array(object: object, encoder: heu.phe.FloatEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyFloatEncoder

7. array(ndarray: numpy.ndarray, encoder: heu.phe.BatchIntegerEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchIntegerEncoder

8. array(object: object, encoder: heu.phe.BatchIntegerEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchIntegerEncoder

9. array(ndarray: numpy.ndarray, encoder: heu.phe.BatchFloatEncoder) -> heu.numpy.PlaintextArray

Create and encode an array using heu::pylib::PyBatchFloatEncoder

10. array(object: object, encoder: heu.phe.BatchFloatEncoder) -> heu.numpy.PlaintextArray

Encode a numpy ndarray using heu::pylib::PyBatchFloatEncoder

class heu.numpy.random

static randbits(schema: heu.phe.SchemaType, bits: int, shape: heu.numpy.Shape) → heu.numpy.PlaintextArray

Return a random integer array where each element is ‘bits’ bits long

static randint(min: heu.phe.Plaintext, max: heu.phe.Plaintext, shape: heu.numpy.Shape) → heu.numpy.PlaintextArray

Return a random integer array from the “discrete uniform” distribution in interval [min, max)

heu.numpy.setup(*args, **kwargs)

Overloaded function.

1. setup(schema_type: heu.phe.SchemaType = <SchemaType.ZPaillier: 3>, key_size: int = 2048) -> heu.numpy.HeKit

Setup phe (numpy) environment by schema type and key size

2. setup(schema_string: str = ‘z-paillier’, key_size: int = 2048) -> heu.numpy.HeKit

Setup phe (numpy) environment by schema string and key size

3. setup(public_key: heu.phe.PublicKey) -> heu.numpy.DestinationHeKit

Setup phe (numpy) environment by an already generated public key

heu.numpy.tree_predict(arg0: numpy.ndarray[numpy.float64[m, n], flags.c_contiguous], arg1: List[int], arg2: List[float]) → numpy.ndarray[numpy.int8[m, n]]

Compute tree predict based on split features and points.