How to use _verify_matrix method in lisa

Best Python code snippet using lisa_python

test_search_space.py

Source:test_search_space.py Github

copy

Full Screen

...39 super().__init__(*args, **kwargs)40 id = f"{'.'.join(self.id().split('.')[-2:])}"41 self._log = get_logger(id)42 def test_supported_intrange(self) -> None:43 self._verify_matrix(44 expected_meet=[45 [True, True, True, False, True, True, False, True, False, False],46 [True, True, False, False, True, True, False, False, False, False],47 ],48 expected_min=[49 [12, 10, 15, False, 10, 10, False, 15, False, False],50 [12, 10, False, False, 10, 10, False, False, False, False],51 ],52 requirements=[53 IntRange(min=10, max=15),54 IntRange(min=10, max=15, max_inclusive=False),55 ],56 capabilities=[57 IntRange(12),58 IntRange(10),59 IntRange(15),60 IntRange(20),61 IntRange(5, 11),62 IntRange(5, 10),63 IntRange(5, 10, max_inclusive=False),64 IntRange(15, 20),65 IntRange(1, 5),66 IntRange(20, 100),67 ],68 )69 def test_supported_countspace(self) -> None:70 expected_meet = [71 [True, True, True, True, True, True, True, True, True, True, True],72 [False, True, False, False, False, True, True, True, False, False, False],73 [False, False, True, False, False, True, False, True, True, False, False],74 [False, False, False, False, True, False, False, True, True, True, True],75 [False, True, True, False, False, True, True, True, True, False, False],76 [False, True, False, False, False, True, True, True, True, False, False],77 [False, True, True, False, True, True, True, True, True, True, True],78 ]79 expected_min: List[List[Any]] = [80 [None, 10, 15, 18, 25, 10, 10, 10, 12, 21, 21],81 [False, 10, False, False, False, 10, 10, 10, False, False, False],82 [False, False, 15, False, False, 15, False, 15, 15, False, False],83 [False, False, False, False, 25, False, False, 25, 25, 25, 25],84 [False, 10, 15, False, False, 10, 10, 10, 12, False, False],85 [False, 10, False, False, False, 10, 10, 10, 12, False, False],86 [False, 10, 15, False, 25, 10, 10, 10, 12, 21, 21],87 ]88 self._verify_matrix(89 expected_meet=expected_meet,90 expected_min=expected_min,91 requirements=[92 None,93 10,94 15,95 25,96 IntRange(min=10, max=15),97 IntRange(min=10, max=15, max_inclusive=False),98 [IntRange(min=10, max=15), IntRange(min=20, max=80)],99 ],100 capabilities=[101 None,102 10,103 15,104 18,105 25,106 IntRange(min=10, max=15),107 IntRange(min=10, max=15, max_inclusive=False),108 [IntRange(min=10, max=15), IntRange(min=20, max=80)],109 [IntRange(min=12, max=30)],110 [IntRange(min=21, max=25)],111 IntRange(min=21, max=25),112 ],113 )114 def test_supported_set_space(self) -> None:115 set_aa = set(["aa"])116 set_aa_bb = set(["aa", "bb"])117 set_aa_bb_cc = set(["aa", "bb", "cc"])118 set_aa_cc = set(["aa", "cc"])119 set_cc = set(["cc"])120 self._verify_matrix(121 expected_meet=[122 [True, True, True, True, True],123 [True, True, True, True, True],124 [False, False, False, True, False],125 [True, False, True, False, False],126 [True, False, True, False, False],127 ],128 expected_min=[129 [None, None, None, None, None],130 [None, None, None, None, None],131 [False, False, False, set_aa_bb, False],132 [None, False, None, False, False],133 [None, False, None, False, False],134 ],135 requirements=[136 SetSpace[str](is_allow_set=True),137 SetSpace[str](is_allow_set=False),138 SetSpace[str](items=set_aa_bb, is_allow_set=True),139 SetSpace[str](items=set_aa_bb),140 SetSpace[str](items=set_aa_bb, is_allow_set=False),141 ],142 capabilities=[143 SetSpace[str](is_allow_set=True),144 SetSpace[str](items=set_aa, is_allow_set=True),145 SetSpace[str](items=set_cc, is_allow_set=True),146 SetSpace[str](items=set_aa_bb_cc, is_allow_set=True),147 SetSpace[str](items=set_aa_cc, is_allow_set=True),148 ],149 )150 def test_generate_min_capability_not_supported(self) -> None:151 requirement = IntRange(min=5)152 capability = IntRange(max=4)153 with self.assertRaises(expected_exception=LisaException) as cm:154 requirement.generate_min_capability(capability)155 self.assertIn("doesn't support", str(cm.exception))156 def test_int_range_validation(self) -> None:157 with self.assertRaises(expected_exception=LisaException) as cm:158 IntRange(min=6, max=4)159 self.assertIn("shouldn't be greater than", str(cm.exception))160 # no exception161 IntRange(min=5, max=5)162 with self.assertRaises(expected_exception=LisaException) as cm:163 IntRange(min=5, max=5, max_inclusive=False)164 self.assertIn("shouldn't be equal to", str(cm.exception))165 def _verify_matrix(166 self,167 expected_meet: List[List[bool]],168 expected_min: List[List[Any]],169 requirements: List[T],170 capabilities: List[T],171 ) -> None:172 for r_index, requirement in enumerate(requirements):173 for c_index, capability in enumerate(capabilities):174 extra_msg = (175 f"index: [{r_index},{c_index}], "176 f"requirement: {requirement}, capability: {capability}"177 )178 if isinstance(requirement, RequirementMixin):179 self._assert_check(...

Full Screen

Full Screen

description.py

Source:description.py Github

copy

Full Screen

...103 """104 if value is None:105 return106 107 self._matrix = self._verify_matrix(value)108 self._matrix_cumsum = cumsum(self._matrix, axis=1)109 def _verify_matrix(self, value: list[list[float]] | ndarray) -> ndarray:110 """returns verified copy of the matrix111 112 values are normalized so the sum of every row is equal to 1.0113 raises ValueError 114 """115 try:116 value = array(value, dtype=float32)117 if value.shape != self.shape:118 raise ValueError('Matrix dimension should be equal to the original dimension')119 120 value /= value.sum(1)[:, newaxis]121 if not allclose(value.sum(1), 1.0):122 raise ValueError('Matrix should be a right-stochastic matrix')123 ...

Full Screen

Full Screen

gauss.py

Source:gauss.py Github

copy

Full Screen

1from helpers import _print_highlighted_matrix2import numpy as np3import numpy.typing as npt4def _verify_matrix(M: npt.ArrayLike) -> bool:5 n: int = M.shape[0]6 return all([M[i][i] != 0 for i in range(n)])7 8def _pivot_matrix(M: npt.ArrayLike, row: int, col: int) -> npt.ArrayLike:9 M[[row, col]] = M[[col, row]]10 return M11def gaussian_elim(A: npt.ArrayLike, b: npt.ArrayLike, verbose: bool=False) -> npt.ArrayLike:12 # recast arrays as floats for 13 # potential decimal calculations 14 # ahead15 A = A.astype('float64')16 b = b.astype('float64')17 18 # concatenate arrays to create19 # an augmented matrix20 aug_A = np.insert(A, len(A), b, axis=1)21 if verbose:22 print(aug_A)23 24 # extract num rows and num cols25 rows: int = aug_A.shape[0]26 cols: int = aug_A.shape[1]27 28 # check that our matrix is valid29 while not _verify_matrix(aug_A):30 # swap rows to achieve valid matrix31 for i in range(rows):32 if aug_A[i][i] == 0:33 print(f"0 pivot element found. Swapping rows {i} and {i-1}")34 aug_A = _pivot_matrix(aug_A, i, i-1)35 36 37 # for each column in the matrix (minus solution set)38 for i in range(cols-1):39 # iterate over each row40 # but only need to start at row below triangle41 # we are creating... i.e. full_col + 142 for j in range(i+1,rows):43 ...

Full Screen

Full Screen

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.

LambdaTest Learning Hubs:

YouTube

You could also refer to video tutorials over LambdaTest YouTube channel to get step by step demonstration from industry experts.

Run lisa automation tests on LambdaTest cloud grid

Perform automation testing on 3000+ real desktop and mobile devices online.

Try LambdaTest Now !!

Get 100 minutes of automation test minutes FREE!!

Next-Gen App & Browser Testing Cloud

Was this article helpful?

Helpful

NotHelpful