Unlock 30% off on Manual Testing Annual Plans this Holiday Season.

Copied to Clipboard!

How to use allow_transition method in hypothesis

Best Python code snippet using hypothesis

test_HMMGeneral.py

Source:test_HMMGeneral.py

...83        self.mm_builder.set_initial_probabilities({'1': 0.9, '2': 0.1})84        test_assertion("Set initial probabilities",85                       self.mm_builder.initial_prob, {'1': 0.9, '2': 0.1})86    def test_set_equal_probabilities(self):87        self.mm_builder.allow_transition('1', '2', 0.05)88        self.mm_builder.allow_transition('2', '1', 0.95)89        self.mm_builder.set_equal_probabilities()90        test_assertion("Equal initial probabilities",91                       self.mm_builder.initial_prob,92                       {'1': 0.5, '2': 0.5})93        test_assertion("Equal transition probabilities",94                       self.mm_builder.transition_prob,95                       {('1', '2'): 0.5, ('2', '1'): 0.5})96        test_assertion("Equal emission probabilities",97                       self.mm_builder.emission_prob,98                       {('2', 'A'): 0.25, ('1', 'B'): 0.25,99                        ('1', 'A'): 0.25, ('2', 'B'): 0.25})100    def test_set_random_probabilities(self):101        self.mm_builder.allow_transition('1', '2', 0.05)102        self.mm_builder.allow_transition('2', '1', 0.95)103        self.mm_builder.set_random_probabilities()104        test_assertion("Number of initial probabilities",105                       len(self.mm_builder.initial_prob),106                       len(self.mm_builder._state_alphabet.letters))107        # To test this more thoroughly, perhaps mock random.random() and108        # verify that it's being called as expected?109class HiddenMarkovModelTest(unittest.TestCase):110    def setUp(self):111        self.mm_builder = MarkovModel.MarkovModelBuilder(NumberAlphabet(),112                                                    LetterAlphabet())113    def test_transitions_from(self):114        """Testing the calculation of transitions_from115        """116        self.mm_builder.allow_transition('1', '2', 1.0)117        self.mm_builder.allow_transition('2', '1', 0.5)118        self.mm_builder.allow_transition('2', '2', 0.5)119        self.mm_builder.set_initial_probabilities({})120        self.mm = self.mm_builder.get_markov_model()121        state_1 = self.mm.transitions_from("1")122        expected_state_1 = ["2"]123        state_1.sort()124        expected_state_1.sort()125        test_assertion("States reached by transitions from state 1",126                       state_1, expected_state_1)127        state_2 = self.mm.transitions_from("2")128        expected_state_2 = ["1", "2"]129        state_2.sort()130        expected_state_2.sort()131        test_assertion("States reached by transitions from state 2",132                       state_2, expected_state_2)133        fake_state = self.mm.transitions_from("Fake")134        expected_fake_state = []135        test_assertion("States reached by transitions from a fake transition",136                       fake_state, expected_fake_state)137    def test_transitions_to(self):138        """Testing the calculation of transitions_to139        """140        self.mm_builder.allow_transition('1', '1', 0.5)141        self.mm_builder.allow_transition('1', '2', 0.5)142        self.mm_builder.allow_transition('2', '1', 1.0)143        self.mm_builder.set_initial_probabilities({})144        self.mm = self.mm_builder.get_markov_model()145        state_1 = self.mm.transitions_to("1")146        expected_state_1 = ["1", "2"]147        state_1.sort()148        expected_state_1.sort()149        test_assertion("States with transitions to state 1",150                       state_1, expected_state_1)151        state_2 = self.mm.transitions_to("2")152        expected_state_2 = ["1"]153        state_2.sort()154        expected_state_2.sort()155        test_assertion("States with transitions to state 2",156                       state_2, expected_state_2)157        fake_state = self.mm.transitions_to("Fake")158        expected_fake_state = []159        test_assertion("States with transitions to a fake transition",160                       fake_state, expected_fake_state)161    def test_allow_transition(self):162        """Testing allow_transition163        """164        self.mm_builder.allow_transition('1', '2', 1.0)165        self.mm_builder.set_initial_probabilities({})166        self.mm = self.mm_builder.get_markov_model()167        state_1 = self.mm.transitions_from("1")168        expected_state_1 = ["2"]169        state_1.sort()170        expected_state_1.sort()171        test_assertion("States reached by transitions from state 1",172                       state_1, expected_state_1)173        state_2 = self.mm.transitions_from("2")174        expected_state_2 = []175        state_2.sort()176        expected_state_2.sort()177        test_assertion("States reached by transitions from state 2",178                       state_2, expected_state_2)179        state_1 = self.mm.transitions_to("1")180        expected_state_1 = []181        state_1.sort()182        expected_state_1.sort()183        test_assertion("States with transitions to state 1",184                       state_1, expected_state_1)185        state_2 = self.mm.transitions_to("2")186        expected_state_2 = ["1"]187        state_2.sort()188        expected_state_2.sort()189        test_assertion("States with transitions to state 2",190                       state_2, expected_state_2)191    def test_simple_hmm(self):192        """Test a simple model with 2 states and 2 symbols.193        """194        # set initial probabilities195        prob_initial = [0.4, 0.6]196        self.mm_builder.set_initial_probabilities(197                {'1': prob_initial[0], '2': prob_initial[1]})198        # set transition probabilities199        prob_transition = [[0.35, 0.65], [0.45, 0.55]]200        self.mm_builder.allow_transition('1', '1', prob_transition[0][0])201        self.mm_builder.allow_transition('1', '2', prob_transition[0][1])202        self.mm_builder.allow_transition('2', '1', prob_transition[1][0])203        self.mm_builder.allow_transition('2', '2', prob_transition[1][1])204        # set emission probabilities205        prob_emission = [[0.45, 0.55], [0.75, 0.25]]206        self.mm_builder.set_emission_score('1', 'A', prob_emission[0][0])207        self.mm_builder.set_emission_score('1', 'B', prob_emission[0][1])208        self.mm_builder.set_emission_score('2', 'A', prob_emission[1][0])209        self.mm_builder.set_emission_score('2', 'B', prob_emission[1][1])210        # Check all two letter sequences using a brute force calculation211        model = self.mm_builder.get_markov_model()212        for first_letter in LetterAlphabet.letters:213            for second_letter in LetterAlphabet.letters:214                observed_emissions = [first_letter, second_letter]215                viterbi = model.viterbi(observed_emissions, NumberAlphabet)216                self._checkSimpleHmm(prob_initial, prob_transition,217                                 prob_emission, viterbi, observed_emissions)218    def _checkSimpleHmm(self, prob_initial, prob_transition, prob_emission,219                         viterbi, observed_emissions):220        max_prob = 0221        # expected first and second states in the sequence, calculated below222        seq_first_state = None223        seq_second_state = None224        # convert the observed letters 'A' or 'B' into 0 or 1225        letter1 = ord(observed_emissions[0]) - ord('A')226        letter2 = ord(observed_emissions[1]) - ord('A')227        for first_state in NumberAlphabet.letters:228            for second_state in NumberAlphabet.letters:229                # compute the probability of the state sequence first_state,230                # second_state emitting the observed_emissions231                state1 = ord(first_state) - ord('1')232                state2 = ord(second_state) - ord('1')233                prob = prob_initial[state1] * prob_emission[state1][letter1] *\234                    prob_transition[state1][state2] *\235                    prob_emission[state2][letter2]236                if prob > max_prob:237                    seq_first_state = first_state238                    seq_second_state = second_state239                    max_prob = prob240        max_prob = math.log(max_prob)241        seq = viterbi[0]242        prob = viterbi[1]243        test_assertion("state sequence",244                       str(seq),245                       seq_first_state + seq_second_state)246        test_assertion("log probability", round(prob, 11), round(max_prob, 11))247    def test_non_ergodic(self):248        """Test a non-ergodic model (meaning that some transitions are not249        allowed).250        """251        # make state '1' the initial state252        prob_1_initial = 1.0253        self.mm_builder.set_initial_probabilities(254                {'1': prob_1_initial})255        # probabilities of transitioning from state 1 to 1, and 1 to 2256        prob_1_to_1 = 0.5257        prob_1_to_2 = 0.5258        # set up allowed transitions259        self.mm_builder.allow_transition('1', '1', prob_1_to_1)260        self.mm_builder.allow_transition('1', '2', prob_1_to_2)261        # Emission probabilities262        # In state 1 the most likely emission is A, in state 2 the most263        # likely emission is B. (Would be simpler just to use 1.0 and 0.0264        # emission probabilities here, but the algorithm blows up on zero265        # probabilities because of the conversion to log space.)266        prob_1_A = 0.95267        prob_1_B = 0.05268        prob_2_A = 0.05269        prob_2_B = 0.95270        # set emission probabilities271        self.mm_builder.set_emission_score('1', 'A', prob_1_A)272        self.mm_builder.set_emission_score('1', 'B', prob_1_B)273        self.mm_builder.set_emission_score('2', 'A', prob_2_A)274        self.mm_builder.set_emission_score('2', 'B', prob_2_B)...

HiddenMarkovModel.py

Source:HiddenMarkovModel.py

...24    25#membangun HMM26hmmbuild = MarkovModel.MarkovModelBuilder(StateAlphabet(),DNAAlphabet())27#transisi dari match state28hmmbuild.allow_transition('M','N')29hmmbuild.allow_transition('N','O')30hmmbuild.allow_transition('M','I')31hmmbuild.allow_transition('N','J')32hmmbuild.allow_transition('O','K')33hmmbuild.allow_transition('M','E')34hmmbuild.allow_transition('N','F')35#transisi dari insert state36hmmbuild.allow_transition('L','M')37hmmbuild.allow_transition('I','N')38hmmbuild.allow_transition('J','O')39hmmbuild.allow_transition('I','I')40hmmbuild.allow_transition('J','J')41hmmbuild.allow_transition('K','K')42hmmbuild.allow_transition('L','L')43#transisi dari delete state 44hmmbuild.allow_transition('D','N')45hmmbuild.allow_transition('E','O')46hmmbuild.allow_transition('D','E')47hmmbuild.allow_transition('E','F')48hmmbuild.allow_transition('N','M')49hmmbuild.allow_transition('O','N')50hmmbuild.allow_transition('I','M')51hmmbuild.allow_transition('J','N')52hmmbuild.allow_transition('K','O')53hmmbuild.allow_transition('M','L')54hmmbuild.allow_transition('N','I')55hmmbuild.allow_transition('O','J')56hmmbuild.allow_transition('E','M')57hmmbuild.allow_transition('F','N')58hmmbuild.allow_transition('N','D')59hmmbuild.allow_transition('O','E')60hmmbuild.allow_transition('E','D')61hmmbuild.allow_transition('F','E')62#initial probability63hmmbuild.set_initial_probabilities({'M': 0.8, 'D': 0.2,'L':0})64#probabilitas untuk transisi dari satu state ke state lain65hmmbuild.set_transition_score('M', 'N', 1)66hmmbuild.set_transition_score('N', 'O', .4)67hmmbuild.set_transition_score('N', 'J', .6)68hmmbuild.set_transition_score('J', 'J', (4/6))69hmmbuild.set_transition_score('J', 'O', (2/6))70hmmbuild.set_transition_score('D', 'N', 1)71#emission probability dari state72hmmbuild.set_emission_score('M', 'A', 1)73hmmbuild.set_emission_score('N', 'G', 1)74hmmbuild.set_emission_score('O', 'C', .8)75hmmbuild.set_emission_score('O', 'G', .2)...

phmm.py

Source:phmm.py

...19    letters = ['M', 'N','O','D','E','F','I','J','K','L']20#inisialisasi hmm builder21builder = MarkovModel.MarkovModelBuilder(State(),DNA())22#transisi dari match state23builder.allow_transition('M','N')24builder.allow_transition('M','E')25builder.allow_transition('M','J')26builder.allow_transition('N','O')27builder.allow_transition('N','K')28builder.allow_transition('O','L')29#transisi dari insert state30builder.allow_transition('I','M')31builder.allow_transition('I','I')32builder.allow_transition('I','D')33builder.allow_transition('J','N')34builder.allow_transition('J','J')35builder.allow_transition('J','E')36builder.allow_transition('K','O')37builder.allow_transition('K','K')38builder.allow_transition('K','F')39builder.allow_transition('L','L')40#transisi dari delete state 41builder.allow_transition('D','E')42builder.allow_transition('D','J')43builder.allow_transition('D','N')44builder.allow_transition('E','F')45builder.allow_transition('E','K')46builder.allow_transition('E','O')47builder.allow_transition('F','L')48#initial probability49builder.set_initial_probabilities({'M': 1})50#probabilitas untuk transisi dari satu state ke state lain51builder.set_transition_score('M', 'N', (1/3))52builder.set_transition_score('M', 'J', (2/3))53builder.set_transition_score('J', 'J', (1/3))54builder.set_transition_score('J', 'N', (2/3))55builder.set_transition_score('N', 'O', (1))56#emission probability dari state57builder.set_emission_score('M', 'A', 1)58builder.set_emission_score('J', 'C', 1)59builder.set_emission_score('N', 'S', 1)60builder.set_emission_score('O', 'A', (1/3))61builder.set_emission_score('O', 'T', (2/3))...

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.