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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))...

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