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nac3/nac3standalone/demo/src/ndarray.py

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core/builtins: Refactored numpy builtins to accept scalar and ndarrays
2024-04-25 15:47:16 +08:00
@extern
def dbl_nan() -> float:
...
@extern
def dbl_inf() -> float:
...
core: Implement elementwise unary operators
2024-03-27 17:06:58 +08:00
@extern
def output_bool(x: bool):
...
core: Add test for indexing into ndarray
2024-02-19 17:10:44 +08:00
@extern
def output_int32(x: int32):
...
core/builtins: Refactored numpy builtins to accept scalar and ndarrays
2024-04-25 15:47:16 +08:00
@extern
def output_int64(x: int64):
...
@extern
def output_uint32(x: uint32):
...
@extern
def output_uint64(x: uint64):
...
core: Add test for indexing into ndarray
2024-02-19 17:10:44 +08:00
@extern
def output_float64(x: float):
...
core: Implement elementwise unary operators
2024-03-27 17:06:58 +08:00
def output_ndarray_bool_2(n: ndarray[bool, Literal[2]]):
for r in range(len(n)):
for c in range(len(n[r])):
output_bool(n[r][c])
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
def output_ndarray_int32_1(n: ndarray[int32, Literal[1]]):
for i in range(len(n)):
output_int32(n[i])
core: Implement elementwise unary operators
2024-03-27 17:06:58 +08:00
def output_ndarray_int32_2(n: ndarray[int32, Literal[2]]):
for r in range(len(n)):
for c in range(len(n[r])):
output_int32(n[r][c])
core/builtins: Refactored numpy builtins to accept scalar and ndarrays
2024-04-25 15:47:16 +08:00
def output_ndarray_int64_2(n: ndarray[int64, Literal[2]]):
for r in range(len(n)):
for c in range(len(n[r])):
output_int64(n[r][c])
def output_ndarray_uint32_2(n: ndarray[uint32, Literal[2]]):
for r in range(len(n)):
for c in range(len(n[r])):
output_uint32(n[r][c])
def output_ndarray_uint64_2(n: ndarray[uint64, Literal[2]]):
for r in range(len(n)):
for c in range(len(n[r])):
output_uint64(n[r][c])
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
def output_ndarray_float_1(n: ndarray[float, Literal[1]]):
for i in range(len(n)):
output_float64(n[i])
def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
for r in range(len(n)):
for c in range(len(n[r])):
output_float64(n[r][c])
core: Implement ndarray constructor and numpy.empty
2023-11-17 17:30:27 +08:00
def consume_ndarray_1(n: ndarray[float, Literal[1]]):
pass
core: support tuple and int32 input for np_empty, np_ones, and more
2024-06-25 15:35:02 +08:00
def consume_ndarray_2(n: ndarray[float, Literal[2]]):
pass
core: Implement ndarray constructor and numpy.empty
2023-11-17 17:30:27 +08:00
def test_ndarray_ctor():
core: Fix Literal use in variable type annotation
2024-02-06 12:29:21 +08:00
n: ndarray[float, Literal[1]] = np_ndarray([1])
core: Implement ndarray constructor and numpy.empty
2023-11-17 17:30:27 +08:00
consume_ndarray_1(n)
def test_ndarray_empty():
core: support tuple and int32 input for np_empty, np_ones, and more
2024-06-25 15:35:02 +08:00
n1: ndarray[float, 1] = np_empty([1])
consume_ndarray_1(n1)
n2: ndarray[float, 1] = np_empty(10)
consume_ndarray_1(n2)
n3: ndarray[float, 1] = np_empty((2,))
consume_ndarray_1(n3)
n4: ndarray[float, 2] = np_empty((4, 4))
consume_ndarray_2(n4)
dim4 = (5, 2)
n5: ndarray[float, 2] = np_empty(dim4)
consume_ndarray_2(n5)
core: Implement ndarray constructor and numpy.empty
2023-11-17 17:30:27 +08:00
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
def test_ndarray_zeros():
core: support tuple and int32 input for np_empty, np_ones, and more
2024-06-25 15:35:02 +08:00
n1: ndarray[float, 1] = np_zeros([1])
output_ndarray_float_1(n1)
k = 3 + int32(n1[0]) # to test variable shape inputs
n2: ndarray[float, 1] = np_zeros(k * k)
output_ndarray_float_1(n2)
n3: ndarray[float, 1] = np_zeros((k * 2,))
output_ndarray_float_1(n3)
dim4 = (3, 2 * k)
n4: ndarray[float, 2] = np_zeros(dim4)
output_ndarray_float_2(n4)
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
def test_ndarray_ones():
core: Fix Literal use in variable type annotation
2024-02-06 12:29:21 +08:00
n: ndarray[float, 1] = np_ones([1])
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
output_ndarray_float_1(n)
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
def test_ndarray_full():
core: Fix Literal use in variable type annotation
2024-02-06 12:29:21 +08:00
n_float: ndarray[float, 1] = np_full([1], 2.0)
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
output_ndarray_float_1(n_float)
core: Fix Literal use in variable type annotation
2024-02-06 12:29:21 +08:00
n_i32: ndarray[int32, 1] = np_full([1], 2)
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
output_ndarray_int32_1(n_i32)
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
def test_ndarray_eye():
core: Fix Literal use in variable type annotation
2024-02-06 12:29:21 +08:00
n: ndarray[float, 2] = np_eye(2)
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
output_ndarray_float_2(n)
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
standalone: Add np_array tests
2024-06-11 15:30:57 +08:00
def test_ndarray_array():
n1: ndarray[float, 1] = np_array([1.0, 2.0, 3.0])
output_ndarray_float_1(n1)
n1to2: ndarray[float, 2] = np_array([1.0, 2.0, 3.0], ndmin=2)
output_ndarray_float_2(n1to2)
n2: ndarray[float, 2] = np_array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
output_ndarray_float_2(n2)
# Copy
n2_cpy: ndarray[float, 2] = np_array(n2, copy=False)
n2_cpy.fill(0.0)
output_ndarray_float_2(n2_cpy)
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
def test_ndarray_identity():
core: Fix Literal use in variable type annotation
2024-02-06 12:29:21 +08:00
n: ndarray[float, 2] = np_identity(2)
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
output_ndarray_float_2(n)
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
core: Implement `ndarray.fill`
2024-03-06 16:53:41 +08:00
def test_ndarray_fill():
n: ndarray[float, 2] = np_empty([2, 2])
n.fill(1.0)
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
output_ndarray_float_2(n)
core: Implement `ndarray.fill`
2024-03-06 16:53:41 +08:00
standalone: Update ndarray.py to output all elements in ndarrays
2024-03-07 13:08:45 +08:00
def test_ndarray_copy():
x: ndarray[float, 2] = np_identity(2)
y = x.copy()
x.fill(0.0)
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
output_ndarray_float_2(x)
output_ndarray_float_2(y)
core/expr: Implement negative indices for ndarray
2024-04-15 12:20:13 +08:00
def test_ndarray_neg_idx():
x = np_identity(2)
for i in range(-1, -3, -1):
for j in range(-1, -3, -1):
output_float64(x[i][j])
standalone: Add test case for ndarray slicing
2024-05-30 16:08:35 +08:00
def test_ndarray_slices():
x = np_identity(3)
output_ndarray_float_2(x)
x_identity = x[::]
output_ndarray_float_2(x_identity)
x02 = x[0::2]
output_ndarray_float_2(x02)
x_mirror = x[::-1]
output_ndarray_float_2(x_mirror)
x2 = x[0::2, 0::2]
output_ndarray_float_2(x2)
standalone: Add test for multidim array index with one index
2024-06-20 11:46:34 +08:00
def test_ndarray_nd_idx():
x = np_identity(2)
x0: float = x[0, 0]
output_float64(x0)
output_float64(x[0, 1])
output_float64(x[1, 0])
output_float64(x[1, 1])
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
def test_ndarray_add():
x = np_identity(2)
y = x + np_ones([2, 2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_add_broadcast():
x = np_identity(2)
# y: ndarray[float, 2] = x + np_ones([2])
y = x + np_ones([2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_add_broadcast_lhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = 1.0 + x
y = 1.0 + x
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_add_broadcast_rhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = x + 1.0
y = x + 1.0
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_iadd():
x = np_identity(2)
x += np_ones([2, 2])
output_ndarray_float_2(x)
def test_ndarray_iadd_broadcast():
x = np_identity(2)
x += np_ones([2])
output_ndarray_float_2(x)
def test_ndarray_iadd_broadcast_scalar():
x = np_identity(2)
x += 1.0
output_ndarray_float_2(x)
def test_ndarray_sub():
x = np_ones([2, 2])
y = x - np_identity(2)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_sub_broadcast():
x = np_identity(2)
# y: ndarray[float, 2] = x - np_ones([2])
y = x - np_ones([2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_sub_broadcast_lhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = 1.0 - x
y = 1.0 - x
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_sub_broadcast_rhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = x - 1
y = x - 1.0
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_isub():
x = np_ones([2, 2])
x -= np_identity(2)
output_ndarray_float_2(x)
def test_ndarray_isub_broadcast():
x = np_identity(2)
x -= np_ones([2])
output_ndarray_float_2(x)
def test_ndarray_isub_broadcast_scalar():
x = np_identity(2)
x -= 1.0
output_ndarray_float_2(x)
def test_ndarray_mul():
x = np_ones([2, 2])
y = x * np_identity(2)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_mul_broadcast():
x = np_identity(2)
# y: ndarray[float, 2] = x * np_ones([2])
y = x * np_ones([2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_mul_broadcast_lhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = 2.0 * x
y = 2.0 * x
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_mul_broadcast_rhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = x * 2.0
y = x * 2.0
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_imul():
x = np_ones([2, 2])
x *= np_identity(2)
output_ndarray_float_2(x)
def test_ndarray_imul_broadcast():
x = np_identity(2)
x *= np_ones([2])
output_ndarray_float_2(x)
def test_ndarray_imul_broadcast_scalar():
x = np_identity(2)
x *= 2.0
output_ndarray_float_2(x)
def test_ndarray_truediv():
x = np_identity(2)
y = x / np_ones([2, 2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_truediv_broadcast():
x = np_identity(2)
# y: ndarray[float, 2] = x / np_ones([2])
y = x / np_ones([2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_truediv_broadcast_lhs_scalar():
x = np_ones([2, 2])
# y: ndarray[float, 2] = 2.0 / x
y = 2.0 / x
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_truediv_broadcast_rhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = x / 2.0
y = x / 2.0
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_itruediv():
x = np_identity(2)
x /= np_ones([2, 2])
output_ndarray_float_2(x)
def test_ndarray_itruediv_broadcast():
x = np_identity(2)
x /= np_ones([2])
output_ndarray_float_2(x)
def test_ndarray_itruediv_broadcast_scalar():
x = np_identity(2)
x /= 2.0
output_ndarray_float_2(x)
def test_ndarray_floordiv():
x = np_identity(2)
y = x // np_ones([2, 2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_floordiv_broadcast():
x = np_identity(2)
# y: ndarray[float, 2] = x // np_ones([2])
y = x // np_ones([2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_floordiv_broadcast_lhs_scalar():
x = np_ones([2, 2])
# y: ndarray[float, 2] = 2.0 // x
y = 2.0 // x
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_floordiv_broadcast_rhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = x // 2.0
y = x // 2.0
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_ifloordiv():
x = np_identity(2)
x //= np_ones([2, 2])
output_ndarray_float_2(x)
def test_ndarray_ifloordiv_broadcast():
x = np_identity(2)
x //= np_ones([2])
output_ndarray_float_2(x)
def test_ndarray_ifloordiv_broadcast_scalar():
x = np_identity(2)
x //= 2.0
output_ndarray_float_2(x)
def test_ndarray_mod():
x = np_identity(2)
y = x % np_full([2, 2], 2.0)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_mod_broadcast():
x = np_identity(2)
# y: ndarray[float, 2] = x % np_ones([2])
y = x % np_full([2], 2.0)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_mod_broadcast_lhs_scalar():
x = np_ones([2, 2])
# y: ndarray[float, 2] = 2.0 % x
y = 2.0 % x
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_mod_broadcast_rhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = x % 2.0
y = x % 2.0
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_imod():
x = np_identity(2)
x %= np_full([2, 2], 2.0)
output_ndarray_float_2(x)
def test_ndarray_imod_broadcast():
x = np_identity(2)
x %= np_full([2], 2.0)
output_ndarray_float_2(x)
def test_ndarray_imod_broadcast_scalar():
x = np_identity(2)
x %= 2.0
output_ndarray_float_2(x)
def test_ndarray_pow():
x = np_identity(2)
y = x ** np_full([2, 2], 2.0)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_pow_broadcast():
x = np_identity(2)
# y: ndarray[float, 2] = x ** np_full([2], 2.0)
y = x ** np_full([2], 2.0)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_pow_broadcast_lhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = 2.0 ** x
y = 2.0 ** x
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_pow_broadcast_rhs_scalar():
x = np_identity(2)
# y: ndarray[float, 2] = x % 2.0
y = x ** 2.0
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_ipow():
x = np_identity(2)
x **= np_full([2, 2], 2.0)
output_ndarray_float_2(x)
def test_ndarray_ipow_broadcast():
x = np_identity(2)
x **= np_full([2], 2.0)
output_ndarray_float_2(x)
def test_ndarray_ipow_broadcast_scalar():
x = np_identity(2)
x **= 2.0
standalone: Update ndarray.py to output all elements in ndarrays
2024-03-07 13:08:45 +08:00
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
output_ndarray_float_2(x)
standalone: Update ndarray.py to output all elements in ndarrays
2024-03-07 13:08:45 +08:00
core: Implement numpy.matmul for 2D-2D ndarrays
2024-04-19 19:00:07 +08:00
def test_ndarray_matmul():
x = np_identity(2)
y = x @ np_ones([2, 2])
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_imatmul():
x = np_identity(2)
x @= np_ones([2, 2])
output_ndarray_float_2(x)
core: Implement elementwise unary operators
2024-03-27 17:06:58 +08:00
def test_ndarray_pos():
x_int32 = np_full([2, 2], -2)
y_int32 = +x_int32
output_ndarray_int32_2(x_int32)
output_ndarray_int32_2(y_int32)
x_float = np_full([2, 2], -2.0)
y_float = +x_float
output_ndarray_float_2(x_float)
output_ndarray_float_2(y_float)
def test_ndarray_neg():
x_int32 = np_full([2, 2], -2)
y_int32 = -x_int32
output_ndarray_int32_2(x_int32)
output_ndarray_int32_2(y_int32)
x_float = np_full([2, 2], 2.0)
y_float = -x_float
output_ndarray_float_2(x_float)
output_ndarray_float_2(y_float)
def test_ndarray_inv():
x_int32 = np_full([2, 2], -2)
y_int32 = ~x_int32
output_ndarray_int32_2(x_int32)
output_ndarray_int32_2(y_int32)
core: Fix __inv__ for i8-based boolean operands
2024-04-12 15:35:36 +08:00
x_bool = np_full([2, 2], True)
y_bool = ~x_bool
output_ndarray_bool_2(x_bool)
output_ndarray_bool_2(y_bool)
core: Implement elementwise comparison operators
2024-03-27 12:57:11 +08:00
def test_ndarray_eq():
x = np_identity(2)
y = x == np_full([2, 2], 0.0)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_eq_broadcast():
x = np_identity(2)
y = x == np_full([2], 0.0)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_eq_broadcast_lhs_scalar():
x = np_identity(2)
y = 0.0 == x
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_eq_broadcast_rhs_scalar():
x = np_identity(2)
y = x == 0.0
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_ne():
x = np_identity(2)
y = x != np_full([2, 2], 0.0)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_ne_broadcast():
x = np_identity(2)
y = x != np_full([2], 0.0)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_ne_broadcast_lhs_scalar():
x = np_identity(2)
y = 0.0 != x
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_ne_broadcast_rhs_scalar():
x = np_identity(2)
y = x != 0.0
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_lt():
x = np_identity(2)
y = x < np_full([2, 2], 1.0)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_lt_broadcast():
x = np_identity(2)
y = x < np_full([2], 1.0)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_lt_broadcast_lhs_scalar():
x = np_identity(2)
y = 1.0 < x
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_lt_broadcast_rhs_scalar():
x = np_identity(2)
y = x < 1.0
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_le():
x = np_identity(2)
y = x <= np_full([2, 2], 0.5)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_le_broadcast():
x = np_identity(2)
y = x <= np_full([2], 0.5)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_le_broadcast_lhs_scalar():
x = np_identity(2)
y = 0.5 <= x
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_le_broadcast_rhs_scalar():
x = np_identity(2)
y = x <= 0.5
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_gt():
x = np_identity(2)
y = x > np_full([2, 2], 0.0)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_gt_broadcast():
x = np_identity(2)
y = x > np_full([2], 0.0)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_gt_broadcast_lhs_scalar():
x = np_identity(2)
y = 0.0 > x
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_gt_broadcast_rhs_scalar():
x = np_identity(2)
y = x > 0.0
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_ge():
x = np_identity(2)
y = x >= np_full([2, 2], 0.5)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_ge_broadcast():
x = np_identity(2)
y = x >= np_full([2], 0.5)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_ge_broadcast_lhs_scalar():
x = np_identity(2)
y = 0.5 >= x
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_ge_broadcast_rhs_scalar():
x = np_identity(2)
y = x >= 0.5
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
core/builtins: Refactored numpy builtins to accept scalar and ndarrays
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def test_ndarray_int32():
x = np_identity(2)
y = int32(x)
output_ndarray_float_2(x)
output_ndarray_int32_2(y)
def test_ndarray_int64():
x = np_identity(2)
y = int64(x)
output_ndarray_float_2(x)
output_ndarray_int64_2(y)
def test_ndarray_uint32():
x = np_identity(2)
y = uint32(x)
output_ndarray_float_2(x)
output_ndarray_uint32_2(y)
def test_ndarray_uint64():
x = np_identity(2)
y = uint64(x)
output_ndarray_float_2(x)
output_ndarray_uint64_2(y)
def test_ndarray_float():
x = np_full([2, 2], 1)
y = float(x)
output_ndarray_int32_2(x)
output_ndarray_float_2(y)
def test_ndarray_bool():
x = np_identity(2)
y = bool(x)
output_ndarray_float_2(x)
output_ndarray_bool_2(y)
def test_ndarray_round():
x = np_identity(2)
xf32 = round(x)
xf64 = round64(x)
xff = np_round(x)
output_ndarray_float_2(x)
output_ndarray_int32_2(xf32)
output_ndarray_int64_2(xf64)
output_ndarray_float_2(xff)
def test_ndarray_floor():
x = np_identity(2)
xf32 = floor(x)
xf64 = floor64(x)
xff = np_floor(x)
output_ndarray_float_2(x)
output_ndarray_int32_2(xf32)
output_ndarray_int64_2(xf64)
output_ndarray_float_2(xff)
def test_ndarray_ceil():
x = np_identity(2)
xf32 = ceil(x)
xf64 = ceil64(x)
xff = np_ceil(x)
output_ndarray_float_2(x)
output_ndarray_int32_2(xf32)
output_ndarray_int64_2(xf64)
output_ndarray_float_2(xff)
core/builtins: Add np_min/np_max
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def test_ndarray_min():
x = np_identity(2)
y = np_min(x)
output_ndarray_float_2(x)
output_float64(y)
core/builtins: Add np_minimum/np_maximum
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def test_ndarray_minimum():
x = np_identity(2)
min_x_zeros = np_minimum(x, np_zeros([2]))
min_x_ones = np_minimum(x, np_zeros([2]))
output_ndarray_float_2(x)
output_ndarray_float_2(min_x_zeros)
output_ndarray_float_2(min_x_ones)
def test_ndarray_minimum_broadcast():
x = np_identity(2)
min_x_zeros = np_minimum(x, np_zeros([2]))
min_x_ones = np_minimum(x, np_zeros([2]))
output_ndarray_float_2(x)
output_ndarray_float_2(min_x_zeros)
output_ndarray_float_2(min_x_ones)
def test_ndarray_minimum_broadcast_lhs_scalar():
x = np_identity(2)
min_x_zeros = np_minimum(0.0, x)
min_x_ones = np_minimum(1.0, x)
output_ndarray_float_2(x)
output_ndarray_float_2(min_x_zeros)
output_ndarray_float_2(min_x_ones)
def test_ndarray_minimum_broadcast_rhs_scalar():
x = np_identity(2)
min_x_zeros = np_minimum(x, 0.0)
min_x_ones = np_minimum(x, 1.0)
output_ndarray_float_2(x)
output_ndarray_float_2(min_x_zeros)
output_ndarray_float_2(min_x_ones)
standalone: Add np_argmax and np_argmin tests
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def test_ndarray_argmin():
x = np_array([[1., 2.], [3., 4.]])
y = np_argmin(x)
output_ndarray_float_2(x)
output_int64(y)
core/builtins: Add np_min/np_max
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def test_ndarray_max():
x = np_identity(2)
y = np_max(x)
output_ndarray_float_2(x)
output_float64(y)
core/builtins: Add np_minimum/np_maximum
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def test_ndarray_maximum():
x = np_identity(2)
max_x_zeros = np_maximum(x, np_zeros([2]))
max_x_ones = np_maximum(x, np_zeros([2]))
output_ndarray_float_2(x)
output_ndarray_float_2(max_x_zeros)
output_ndarray_float_2(max_x_ones)
def test_ndarray_maximum_broadcast():
x = np_identity(2)
max_x_zeros = np_maximum(x, np_zeros([2]))
max_x_ones = np_maximum(x, np_zeros([2]))
output_ndarray_float_2(x)
output_ndarray_float_2(max_x_zeros)
output_ndarray_float_2(max_x_ones)
def test_ndarray_maximum_broadcast_lhs_scalar():
x = np_identity(2)
max_x_zeros = np_maximum(0.0, x)
max_x_ones = np_maximum(1.0, x)
output_ndarray_float_2(x)
output_ndarray_float_2(max_x_zeros)
output_ndarray_float_2(max_x_ones)
def test_ndarray_maximum_broadcast_rhs_scalar():
x = np_identity(2)
max_x_zeros = np_maximum(x, 0.0)
max_x_ones = np_maximum(x, 1.0)
output_ndarray_float_2(x)
output_ndarray_float_2(max_x_zeros)
output_ndarray_float_2(max_x_ones)
standalone: Add np_argmax and np_argmin tests
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def test_ndarray_argmax():
x = np_array([[1., 2.], [3., 4.]])
y = np_argmax(x)
output_ndarray_float_2(x)
output_int64(y)
core/builtins: Refactored numpy builtins to accept scalar and ndarrays
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def test_ndarray_abs():
x = np_identity(2)
y = abs(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_isnan():
x = np_identity(2)
x_isnan = np_isnan(x)
y = np_full([2, 2], dbl_nan())
y_isnan = np_isnan(y)
output_ndarray_float_2(x)
output_ndarray_bool_2(x_isnan)
output_ndarray_float_2(y)
output_ndarray_bool_2(y_isnan)
def test_ndarray_isinf():
x = np_identity(2)
x_isinf = np_isinf(x)
y = np_full([2, 2], dbl_inf())
y_isinf = np_isinf(y)
output_ndarray_float_2(x)
output_ndarray_bool_2(x_isinf)
output_ndarray_float_2(y)
output_ndarray_bool_2(y_isinf)
def test_ndarray_sin():
x = np_identity(2)
y = np_sin(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_cos():
x = np_identity(2)
y = np_cos(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_exp():
x = np_identity(2)
y = np_exp(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_exp2():
x = np_identity(2)
y = np_exp2(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_log():
x = np_identity(2)
y = np_log(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_log10():
x = np_identity(2)
y = np_log10(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_log2():
x = np_identity(2)
y = np_log2(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_fabs():
x = -np_identity(2)
y = np_fabs(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_sqrt():
x = np_identity(2)
y = np_sqrt(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_rint():
x = np_identity(2)
y = np_rint(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_tan():
x = np_identity(2)
y = np_tan(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_arcsin():
x = np_identity(2)
y = np_arcsin(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_arccos():
x = np_identity(2)
y = np_arccos(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_arctan():
x = np_identity(2)
y = np_arctan(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_sinh():
x = np_identity(2)
y = np_sinh(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_cosh():
x = np_identity(2)
y = np_cosh(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_tanh():
x = np_identity(2)
y = np_tanh(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_arcsinh():
x = np_identity(2)
y = np_arcsinh(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_arccosh():
x = np_identity(2)
y = np_arccosh(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_arctanh():
x = np_identity(2)
y = np_arctanh(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_expm1():
x = np_identity(2)
y = np_expm1(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_cbrt():
x = np_identity(2)
y = np_cbrt(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_erf():
x = np_identity(2)
y = sp_spec_erf(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_erfc():
x = np_identity(2)
y = sp_spec_erfc(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_gamma():
x = np_identity(2)
y = sp_spec_gamma(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_gammaln():
x = np_identity(2)
y = sp_spec_gammaln(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_j0():
x = np_identity(2)
y = sp_spec_j0(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_j1():
x = np_identity(2)
y = sp_spec_j1(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_arctan2():
x = np_identity(2)
zeros = np_zeros([2, 2])
ones = np_ones([2, 2])
atan2_x_zeros = np_arctan2(x, zeros)
atan2_x_ones = np_arctan2(x, ones)
output_ndarray_float_2(x)
output_ndarray_float_2(zeros)
output_ndarray_float_2(ones)
output_ndarray_float_2(atan2_x_zeros)
output_ndarray_float_2(atan2_x_ones)
def test_ndarray_arctan2_broadcast():
x = np_identity(2)
atan2_x_zeros = np_arctan2(x, np_zeros([2]))
atan2_x_ones = np_arctan2(x, np_ones([2]))
output_ndarray_float_2(x)
output_ndarray_float_2(atan2_x_zeros)
output_ndarray_float_2(atan2_x_ones)
def test_ndarray_arctan2_broadcast_lhs_scalar():
x = np_identity(2)
atan2_x_zeros = np_arctan2(0.0, x)
atan2_x_ones = np_arctan2(1.0, x)
output_ndarray_float_2(x)
output_ndarray_float_2(atan2_x_zeros)
output_ndarray_float_2(atan2_x_ones)
def test_ndarray_arctan2_broadcast_rhs_scalar():
x = np_identity(2)
atan2_x_zeros = np_arctan2(x, 0.0)
atan2_x_ones = np_arctan2(x, 1.0)
output_ndarray_float_2(x)
output_ndarray_float_2(atan2_x_zeros)
output_ndarray_float_2(atan2_x_ones)
def test_ndarray_copysign():
x = np_identity(2)
ones = np_ones([2, 2])
negones = np_full([2, 2], -1.0)
copysign_x_ones = np_copysign(x, ones)
copysign_x_negones = np_copysign(x, ones)
output_ndarray_float_2(x)
output_ndarray_float_2(ones)
output_ndarray_float_2(negones)
output_ndarray_float_2(copysign_x_ones)
output_ndarray_float_2(copysign_x_negones)
def test_ndarray_copysign_broadcast():
x = np_identity(2)
copysign_x_ones = np_copysign(x, np_ones([2]))
copysign_x_negones = np_copysign(x, np_full([2], -1.0))
output_ndarray_float_2(x)
output_ndarray_float_2(copysign_x_ones)
output_ndarray_float_2(copysign_x_negones)
def test_ndarray_copysign_broadcast_lhs_scalar():
x = np_identity(2)
copysign_x_ones = np_copysign(1.0, x)
copysign_x_negones = np_copysign(-1.0, x)
output_ndarray_float_2(x)
output_ndarray_float_2(copysign_x_ones)
output_ndarray_float_2(copysign_x_negones)
def test_ndarray_copysign_broadcast_rhs_scalar():
x = np_identity(2)
copysign_x_ones = np_copysign(x, 1.0)
copysign_x_negones = np_copysign(x, -1.0)
output_ndarray_float_2(x)
output_ndarray_float_2(copysign_x_ones)
output_ndarray_float_2(copysign_x_negones)
def test_ndarray_fmax():
x = np_identity(2)
ones = np_ones([2, 2])
negones = np_full([2, 2], -1.0)
fmax_x_ones = np_fmax(x, ones)
fmax_x_negones = np_fmax(x, ones)
output_ndarray_float_2(x)
output_ndarray_float_2(ones)
output_ndarray_float_2(negones)
output_ndarray_float_2(fmax_x_ones)
output_ndarray_float_2(fmax_x_negones)
def test_ndarray_fmax_broadcast():
x = np_identity(2)
fmax_x_ones = np_fmax(x, np_ones([2]))
fmax_x_negones = np_fmax(x, np_full([2], -1.0))
output_ndarray_float_2(x)
output_ndarray_float_2(fmax_x_ones)
output_ndarray_float_2(fmax_x_negones)
def test_ndarray_fmax_broadcast_lhs_scalar():
x = np_identity(2)
fmax_x_ones = np_fmax(1.0, x)
fmax_x_negones = np_fmax(-1.0, x)
output_ndarray_float_2(x)
output_ndarray_float_2(fmax_x_ones)
output_ndarray_float_2(fmax_x_negones)
def test_ndarray_fmax_broadcast_rhs_scalar():
x = np_identity(2)
fmax_x_ones = np_fmax(x, 1.0)
fmax_x_negones = np_fmax(x, -1.0)
output_ndarray_float_2(x)
output_ndarray_float_2(fmax_x_ones)
output_ndarray_float_2(fmax_x_negones)
def test_ndarray_fmin():
x = np_identity(2)
ones = np_ones([2, 2])
negones = np_full([2, 2], -1.0)
fmin_x_ones = np_fmin(x, ones)
fmin_x_negones = np_fmin(x, ones)
output_ndarray_float_2(x)
output_ndarray_float_2(ones)
output_ndarray_float_2(negones)
output_ndarray_float_2(fmin_x_ones)
output_ndarray_float_2(fmin_x_negones)
def test_ndarray_fmin_broadcast():
x = np_identity(2)
fmin_x_ones = np_fmin(x, np_ones([2]))
fmin_x_negones = np_fmin(x, np_full([2], -1.0))
output_ndarray_float_2(x)
output_ndarray_float_2(fmin_x_ones)
output_ndarray_float_2(fmin_x_negones)
def test_ndarray_fmin_broadcast_lhs_scalar():
x = np_identity(2)
fmin_x_ones = np_fmin(1.0, x)
fmin_x_negones = np_fmin(-1.0, x)
output_ndarray_float_2(x)
output_ndarray_float_2(fmin_x_ones)
output_ndarray_float_2(fmin_x_negones)
def test_ndarray_fmin_broadcast_rhs_scalar():
x = np_identity(2)
fmin_x_ones = np_fmin(x, 1.0)
fmin_x_negones = np_fmin(x, -1.0)
output_ndarray_float_2(x)
output_ndarray_float_2(fmin_x_ones)
output_ndarray_float_2(fmin_x_negones)
def test_ndarray_ldexp():
x = np_identity(2)
zeros = np_full([2, 2], 0)
ones = np_full([2, 2], 1)
ldexp_x_zeros = np_ldexp(x, zeros)
ldexp_x_ones = np_ldexp(x, ones)
output_ndarray_float_2(x)
output_ndarray_int32_2(zeros)
output_ndarray_int32_2(ones)
output_ndarray_float_2(ldexp_x_zeros)
output_ndarray_float_2(ldexp_x_ones)
def test_ndarray_ldexp_broadcast():
x = np_identity(2)
ldexp_x_zeros = np_ldexp(x, np_full([2], 0))
ldexp_x_ones = np_ldexp(x, np_full([2], 1))
output_ndarray_float_2(x)
output_ndarray_float_2(ldexp_x_zeros)
output_ndarray_float_2(ldexp_x_ones)
def test_ndarray_ldexp_broadcast_lhs_scalar():
x = int32(np_identity(2))
ldexp_x_zeros = np_ldexp(0.0, x)
ldexp_x_ones = np_ldexp(1.0, x)
output_ndarray_int32_2(x)
output_ndarray_float_2(ldexp_x_zeros)
output_ndarray_float_2(ldexp_x_ones)
def test_ndarray_ldexp_broadcast_rhs_scalar():
x = np_identity(2)
ldexp_x_zeros = np_ldexp(x, 0)
ldexp_x_ones = np_ldexp(x, 1)
output_ndarray_float_2(x)
output_ndarray_float_2(ldexp_x_zeros)
output_ndarray_float_2(ldexp_x_ones)
def test_ndarray_hypot():
x = np_identity(2)
zeros = np_zeros([2, 2])
ones = np_ones([2, 2])
hypot_x_zeros = np_hypot(x, zeros)
hypot_x_ones = np_hypot(x, ones)
output_ndarray_float_2(x)
output_ndarray_float_2(zeros)
output_ndarray_float_2(ones)
output_ndarray_float_2(hypot_x_zeros)
output_ndarray_float_2(hypot_x_ones)
def test_ndarray_hypot_broadcast():
x = np_identity(2)
hypot_x_zeros = np_hypot(x, np_zeros([2]))
hypot_x_ones = np_hypot(x, np_ones([2]))
output_ndarray_float_2(x)
output_ndarray_float_2(hypot_x_zeros)
output_ndarray_float_2(hypot_x_ones)
def test_ndarray_hypot_broadcast_lhs_scalar():
x = np_identity(2)
hypot_x_zeros = np_hypot(0.0, x)
hypot_x_ones = np_hypot(1.0, x)
output_ndarray_float_2(x)
output_ndarray_float_2(hypot_x_zeros)
output_ndarray_float_2(hypot_x_ones)
def test_ndarray_hypot_broadcast_rhs_scalar():
x = np_identity(2)
hypot_x_zeros = np_hypot(x, 0.0)
hypot_x_ones = np_hypot(x, 1.0)
output_ndarray_float_2(x)
output_ndarray_float_2(hypot_x_zeros)
output_ndarray_float_2(hypot_x_ones)
def test_ndarray_nextafter():
x = np_identity(2)
zeros = np_zeros([2, 2])
ones = np_ones([2, 2])
nextafter_x_zeros = np_nextafter(x, zeros)
nextafter_x_ones = np_nextafter(x, ones)
output_ndarray_float_2(x)
output_ndarray_float_2(zeros)
output_ndarray_float_2(ones)
output_ndarray_float_2(nextafter_x_zeros)
output_ndarray_float_2(nextafter_x_ones)
def test_ndarray_nextafter_broadcast():
x = np_identity(2)
nextafter_x_zeros = np_nextafter(x, np_zeros([2]))
nextafter_x_ones = np_nextafter(x, np_ones([2]))
output_ndarray_float_2(x)
output_ndarray_float_2(nextafter_x_zeros)
output_ndarray_float_2(nextafter_x_ones)
def test_ndarray_nextafter_broadcast_lhs_scalar():
x = np_identity(2)
nextafter_x_zeros = np_nextafter(0.0, x)
nextafter_x_ones = np_nextafter(1.0, x)
output_ndarray_float_2(x)
output_ndarray_float_2(nextafter_x_zeros)
output_ndarray_float_2(nextafter_x_ones)
def test_ndarray_nextafter_broadcast_rhs_scalar():
x = np_identity(2)
nextafter_x_zeros = np_nextafter(x, 0.0)
nextafter_x_ones = np_nextafter(x, 1.0)
output_ndarray_float_2(x)
output_ndarray_float_2(nextafter_x_zeros)
output_ndarray_float_2(nextafter_x_ones)
standalone: add np.transpose and np.reshape functions
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def test_ndarray_transpose():
x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
y = np_transpose(x)
z = np_transpose(y)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_reshape():
w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
x = np_reshape(w, (1, 2, 1, -1))
y = np_reshape(x, [2, -1])
z = np_reshape(y, 10)
x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
output_ndarray_float_1(w)
output_ndarray_float_2(y)
output_ndarray_float_1(z)
standalone: add linalg methods and tests
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def test_ndarray_dot():
core: implement np_dot using LLVM_IR
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x1: ndarray[float, 1] = np_array([5.0, 1.0, 4.0, 2.0])
y1: ndarray[float, 1] = np_array([5.0, 1.0, 6.0, 6.0])
z1 = np_dot(x1, y1)
x2: ndarray[int32, 1] = np_array([5, 1, 4, 2])
y2: ndarray[int32, 1] = np_array([5, 1, 6, 6])
z2 = np_dot(x2, y2)
x3: ndarray[bool, 1] = np_array([True, True, True, True])
y3: ndarray[bool, 1] = np_array([True, True, True, True])
z3 = np_dot(x3, y3)
z4 = np_dot(2, 3)
z5 = np_dot(2., 3.)
z6 = np_dot(True, False)
output_float64(z1)
output_int32(z2)
output_bool(z3)
output_int32(z4)
output_float64(z5)
output_bool(z6)
standalone: add linalg methods and tests
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def test_ndarray_linalg_matmul():
x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
y: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
z = np_linalg_matmul(x, y)
m = np_argmax(z)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
output_ndarray_float_2(z)
output_int64(m)
def test_ndarray_cholesky():
x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
y = np_linalg_cholesky(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_qr():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
y, z = np_linalg_qr(x)
output_ndarray_float_2(x)
# QR Factorization is not unique and gives different results in numpy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(y, z)
output_ndarray_float_2(a)
def test_ndarray_linalg_inv():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
y = np_linalg_inv(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_pinv():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
y = np_linalg_pinv(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_schur():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
t, z = sp_linalg_schur(x)
output_ndarray_float_2(x)
# Schur Factorization is not unique and gives different results in scipy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(np_linalg_matmul(z, t), np_linalg_inv(z))
output_ndarray_float_2(a)
def test_ndarray_hessenberg():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 5.0, 8.5]])
h, q = sp_linalg_hessenberg(x)
output_ndarray_float_2(x)
# Hessenberg Factorization is not unique and gives different results in scipy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(np_linalg_matmul(q, h), np_linalg_inv(q))
output_ndarray_float_2(a)
def test_ndarray_lu():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
l, u = sp_linalg_lu(x)
output_ndarray_float_2(x)
output_ndarray_float_2(l)
output_ndarray_float_2(u)
def test_ndarray_svd():
w: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
x, y, z = np_linalg_svd(w)
output_ndarray_float_2(w)
# SVD Factorization is not unique and gives different results in numpy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(x, z)
output_ndarray_float_2(a)
output_ndarray_float_1(y)
core: Implement ndarray constructor and numpy.empty
2023-11-17 17:30:27 +08:00
def run() -> int32:
test_ndarray_ctor()
test_ndarray_empty()
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
test_ndarray_zeros()
test_ndarray_ones()
test_ndarray_full()
test_ndarray_eye()
standalone: Add np_array tests
2024-06-11 15:30:57 +08:00
test_ndarray_array()
core: Implement most ndarray-creation functions
2023-11-27 13:25:53 +08:00
test_ndarray_identity()
core: Implement `ndarray.fill`
2024-03-06 16:53:41 +08:00
test_ndarray_fill()
standalone: Update ndarray.py to output all elements in ndarrays
2024-03-07 13:08:45 +08:00
test_ndarray_copy()
standalone: Add test case for ndarray slicing
2024-05-30 16:08:35 +08:00
core/expr: Implement negative indices for ndarray
2024-04-15 12:20:13 +08:00
test_ndarray_neg_idx()
standalone: Add test case for ndarray slicing
2024-05-30 16:08:35 +08:00
test_ndarray_slices()
standalone: Add test for multidim array index with one index
2024-06-20 11:46:34 +08:00
test_ndarray_nd_idx()
standalone: Add test case for ndarray slicing
2024-05-30 16:08:35 +08:00
core: Implement elementwise binary operators Including immediate variants of these operators.
2024-03-13 11:16:23 +08:00
test_ndarray_add()
test_ndarray_add_broadcast()
test_ndarray_add_broadcast_lhs_scalar()
test_ndarray_add_broadcast_rhs_scalar()
test_ndarray_iadd()
test_ndarray_iadd_broadcast()
test_ndarray_iadd_broadcast_scalar()
test_ndarray_sub()
test_ndarray_sub_broadcast()
test_ndarray_sub_broadcast_lhs_scalar()
test_ndarray_sub_broadcast_rhs_scalar()
test_ndarray_isub()
test_ndarray_isub_broadcast()
test_ndarray_isub_broadcast_scalar()
test_ndarray_mul()
test_ndarray_mul_broadcast()
test_ndarray_mul_broadcast_lhs_scalar()
test_ndarray_mul_broadcast_rhs_scalar()
test_ndarray_imul()
test_ndarray_imul_broadcast()
test_ndarray_imul_broadcast_scalar()
test_ndarray_truediv()
test_ndarray_truediv_broadcast()
test_ndarray_truediv_broadcast_lhs_scalar()
test_ndarray_truediv_broadcast_rhs_scalar()
test_ndarray_itruediv()
test_ndarray_itruediv_broadcast()
test_ndarray_itruediv_broadcast_scalar()
test_ndarray_floordiv()
test_ndarray_floordiv_broadcast()
test_ndarray_floordiv_broadcast_lhs_scalar()
test_ndarray_floordiv_broadcast_rhs_scalar()
test_ndarray_ifloordiv()
test_ndarray_ifloordiv_broadcast()
test_ndarray_ifloordiv_broadcast_scalar()
test_ndarray_mod()
test_ndarray_mod_broadcast()
test_ndarray_mod_broadcast_lhs_scalar()
test_ndarray_mod_broadcast_rhs_scalar()
test_ndarray_imod()
test_ndarray_imod_broadcast()
test_ndarray_imod_broadcast_scalar()
test_ndarray_pow()
test_ndarray_pow_broadcast()
test_ndarray_pow_broadcast_lhs_scalar()
test_ndarray_pow_broadcast_rhs_scalar()
test_ndarray_ipow()
test_ndarray_ipow_broadcast()
test_ndarray_ipow_broadcast_scalar()
core: Implement numpy.matmul for 2D-2D ndarrays
2024-04-19 19:00:07 +08:00
test_ndarray_matmul()
test_ndarray_imatmul()
core: Implement elementwise unary operators
2024-03-27 17:06:58 +08:00
test_ndarray_pos()
test_ndarray_neg()
test_ndarray_inv()
core: Implement elementwise comparison operators
2024-03-27 12:57:11 +08:00
test_ndarray_eq()
test_ndarray_eq_broadcast()
test_ndarray_eq_broadcast_lhs_scalar()
test_ndarray_eq_broadcast_rhs_scalar()
test_ndarray_ne()
test_ndarray_ne_broadcast()
test_ndarray_ne_broadcast_lhs_scalar()
test_ndarray_ne_broadcast_rhs_scalar()
test_ndarray_lt()
test_ndarray_lt_broadcast()
test_ndarray_lt_broadcast_lhs_scalar()
test_ndarray_lt_broadcast_rhs_scalar()
test_ndarray_lt()
test_ndarray_le_broadcast()
test_ndarray_le_broadcast_lhs_scalar()
test_ndarray_le_broadcast_rhs_scalar()
test_ndarray_gt()
test_ndarray_gt_broadcast()
test_ndarray_gt_broadcast_lhs_scalar()
test_ndarray_gt_broadcast_rhs_scalar()
test_ndarray_gt()
test_ndarray_ge_broadcast()
test_ndarray_ge_broadcast_lhs_scalar()
test_ndarray_ge_broadcast_rhs_scalar()
core: Implement ndarray constructor and numpy.empty
2023-11-17 17:30:27 +08:00
core/builtins: Refactored numpy builtins to accept scalar and ndarrays
2024-04-25 15:47:16 +08:00
test_ndarray_int32()
test_ndarray_int64()
test_ndarray_uint32()
test_ndarray_uint64()
test_ndarray_float()
test_ndarray_bool()
test_ndarray_round()
test_ndarray_floor()
core/builtins: Add np_min/np_max
2024-05-08 17:42:19 +08:00
test_ndarray_min()
core/builtins: Add np_minimum/np_maximum
2024-05-08 18:29:11 +08:00
test_ndarray_minimum()
test_ndarray_minimum_broadcast()
test_ndarray_minimum_broadcast_lhs_scalar()
test_ndarray_minimum_broadcast_rhs_scalar()
standalone: Add np_argmax and np_argmin tests
2024-07-12 18:19:56 +08:00
test_ndarray_argmin()
core/builtins: Add np_min/np_max
2024-05-08 17:42:19 +08:00
test_ndarray_max()
core/builtins: Add np_minimum/np_maximum
2024-05-08 18:29:11 +08:00
test_ndarray_maximum()
test_ndarray_maximum_broadcast()
test_ndarray_maximum_broadcast_lhs_scalar()
test_ndarray_maximum_broadcast_rhs_scalar()
standalone: Add np_argmax and np_argmin tests
2024-07-12 18:19:56 +08:00
test_ndarray_argmax()
core/builtins: Refactored numpy builtins to accept scalar and ndarrays
2024-04-25 15:47:16 +08:00
test_ndarray_abs()
test_ndarray_isnan()
test_ndarray_isinf()
test_ndarray_sin()
test_ndarray_cos()
test_ndarray_exp()
test_ndarray_exp2()
test_ndarray_log()
test_ndarray_log10()
test_ndarray_log2()
test_ndarray_fabs()
test_ndarray_sqrt()
test_ndarray_rint()
test_ndarray_tan()
test_ndarray_arcsin()
test_ndarray_arccos()
test_ndarray_arctan()
test_ndarray_sinh()
test_ndarray_cosh()
test_ndarray_tanh()
test_ndarray_arcsinh()
test_ndarray_arccosh()
test_ndarray_arctanh()
test_ndarray_expm1()
test_ndarray_cbrt()
test_ndarray_erf()
test_ndarray_erfc()
test_ndarray_gamma()
test_ndarray_gammaln()
test_ndarray_j0()
test_ndarray_j1()
test_ndarray_arctan2()
test_ndarray_arctan2_broadcast()
test_ndarray_arctan2_broadcast_lhs_scalar()
test_ndarray_arctan2_broadcast_rhs_scalar()
test_ndarray_copysign()
test_ndarray_copysign_broadcast()
test_ndarray_copysign_broadcast_lhs_scalar()
test_ndarray_copysign_broadcast_rhs_scalar()
test_ndarray_fmax()
test_ndarray_fmax_broadcast()
test_ndarray_fmax_broadcast_lhs_scalar()
test_ndarray_fmax_broadcast_rhs_scalar()
test_ndarray_fmin()
test_ndarray_fmin_broadcast()
test_ndarray_fmin_broadcast_lhs_scalar()
test_ndarray_fmin_broadcast_rhs_scalar()
test_ndarray_ldexp()
test_ndarray_ldexp_broadcast()
test_ndarray_ldexp_broadcast_lhs_scalar()
test_ndarray_ldexp_broadcast_rhs_scalar()
test_ndarray_hypot()
test_ndarray_hypot_broadcast()
test_ndarray_hypot_broadcast_lhs_scalar()
test_ndarray_hypot_broadcast_rhs_scalar()
test_ndarray_nextafter()
test_ndarray_nextafter_broadcast()
test_ndarray_nextafter_broadcast_lhs_scalar()
test_ndarray_nextafter_broadcast_rhs_scalar()
standalone: add np.transpose and np.reshape functions
2024-07-31 13:17:06 +08:00
test_ndarray_transpose()
test_ndarray_reshape()
standalone: add linalg methods and tests
2024-07-25 12:17:32 +08:00
test_ndarray_dot()
test_ndarray_linalg_matmul()
test_ndarray_cholesky()
test_ndarray_qr()
test_ndarray_svd()
test_ndarray_linalg_inv()
test_ndarray_pinv()
test_ndarray_lu()
test_ndarray_schur()
test_ndarray_hessenberg()
core: Implement ndarray constructor and numpy.empty
2023-11-17 17:30:27 +08:00
return 0