Join the 1-day Testing & QA Summit featuring 15+ Expert Speakers.Register for FREE! Join TestMu Conference

How to use get_parameters method in localstack

Best Python code snippet using localstack_python

utils.py

Source:utils.py

...5    validate_fact_type,6    validate_month,7    validate_number,8)9def get_validated_get_parameters(get_parameters: QueryDict) -> dict:10    response = {11        "number": "random",12        "fact_type": None,13        "date": None,14        "error": None,15    }16    # Validate number17    if "number" in get_parameters:18        number = get_parameters["number"]19        if validate_number(number) == False:20            response[21                "error"22            ] = "Inform a valid number option: an integer or the word random"23            return response24        response["number"] = number25    # Validate fact type26    if "fact_type" in get_parameters:27        fact_type = get_parameters["fact_type"]28        if validate_fact_type(fact_type) == False:29            response[30                "error"31            ] = "Invalid fact type. Should be one of: trivia, math, date or year"32            return response33        response["fact_type"] = fact_type34    # Validate date request, which should have valid 'day' and 'month' parameters,35    # paired with the "date" 'fact_type' parameter36    if "month" in get_parameters:37        if "day" in get_parameters:38            month = get_parameters["month"]39            if validate_month(month) == False:40                response["error"] = "Invalid month. Should be a number from 1-12"41                return response42            day = get_parameters["day"]43            if validate_day(day) == False:44                response["error"] = "Invalid day. Should be a number from 1-31"45                return response46            date = f"{month}/{day}"47            if validate_date(date) == False:48                response["error"] = "Invalid date. Should be a valid month/day pair"49                return response50            if date is not None and response["fact_type"] != "date":51                response[52                    "error"53                ] = "Invalid date/fact type pair. Should be a valid month/day paired with the fact_type 'date'"54                return response55            response["number"] = date56    return response57def join_numbersapi_get_parameters(get_parameters: QueryDict) -> str:58    # Always appends json as a default get parameter59    response = "?json"60    if "fragment" in get_parameters:61        response += "&fragment"62    if "notfound" in get_parameters:63        response += f"&notfound={get_parameters['notfound']}"64    if "default" in get_parameters:65        response += f"&default={get_parameters['default']}"66    if "min" in get_parameters:67        if "max" in get_parameters:68            if get_parameters["min"] != "" and get_parameters["max"] != "":69                response += f"&min={get_parameters['min']}&max={get_parameters['max']}"70    return response71def join_numbersapi_get_url(get_parameters: dict) -> str:...

nnds.py

Source:nnds.py

...14    def forward(self, q_Xt, q_At):15        Xt, At = q_Xt.sample()[0], q_At.sample()[0]16        return util.map_network(self.network)(T.concatenate([Xt, At], -1))17    def get_dynamics(self):18        return self.network.get_parameters()19    def __getstate__(self):20        state = super(NNDS, self).__getstate__()21        state['architecture'] = self.architecture22        state['weights'] = T.get_current_session().run(self.get_parameters())23        return state24    def __setstate__(self, state):25        network = pickle.loads(state.pop('architecture'))26        weights = state.pop('weights')27        self.__init__(state['ds'], state['da'], network=network)28        T.get_current_session().run([T.core.assign(a, b) for a, b in zip(self.get_parameters(), weights)])29    def get_parameters(self):30        return self.network.get_parameters()31    def kl_divergence(self, q_X, q_A, _):32        # q_Xt - [N, H, ds]33        # q_At - [N, H, da]34        if (q_X, q_A) not in self.cache:35            q_Xt = q_X.__class__([36                q_X.get_parameters('regular')[0][:, :-1],37                q_X.get_parameters('regular')[1][:, :-1],38            ])39            q_At = q_A.__class__([40                q_A.get_parameters('regular')[0][:, :-1],41                q_A.get_parameters('regular')[1][:, :-1],42            ])43            p_Xt1 = self.forward(q_Xt, q_At)44            q_Xt1 = q_X.__class__([45                q_X.get_parameters('regular')[0][:, 1:],46                q_X.get_parameters('regular')[1][:, 1:],47            ])48            rmse = T.sqrt(T.sum(T.square(q_Xt1.get_parameters('regular')[1] - p_Xt1.get_parameters('regular')[1]), axis=-1))49            model_stdev = T.sqrt(p_Xt1.get_parameters('regular')[0])50            encoding_stdev = T.sqrt(q_Xt1.get_parameters('regular')[0])51            self.cache[(q_X, q_A)] = T.sum(stats.kl_divergence(q_Xt1, p_Xt1), axis=-1), {'rmse': rmse, 'encoding-stdev': encoding_stdev, 'model-stdev': model_stdev}52        return self.cache[(q_X, q_A)]53    def next_state(self, state, action, t):54        state_action = T.concatenate([state, action], -1)55        sigma, delta_mu = self.network(state_action).get_parameters('regular')56        return stats.Gaussian([57            sigma,58            delta_mu + state,59        ])60    def kl_gradients(self, q_X, q_A, kl, num_data):61        return T.grad(kl, self.get_parameters())62    def is_filtering_prior(self):...

train.py

Source:train.py

1from client import MLPClient2if __name__ == "__main__":3    model = MLPClient(split="all", l2=1e-3)4    loss, _, accuracy = model.evaluate(model.get_parameters())5    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")6    model.fit(model.get_parameters(), epochs=250)7    loss, _, accuracy = model.evaluate(model.get_parameters())8    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")9    model.fit(model.get_parameters(), epochs=350)10    loss, _, accuracy = model.evaluate(model.get_parameters())11    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")12    model.fit(model.get_parameters(), epochs=450)13    loss, _, accuracy = model.evaluate(model.get_parameters())14    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")15    model.fit(model.get_parameters(), epochs=550)16    loss, _, accuracy = model.evaluate(model.get_parameters())17    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")18    model.fit(model.get_parameters(), epochs=550)19    loss, _, accuracy = model.evaluate(model.get_parameters())...

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.