Python dotmap.DotMap方法代碼示例

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def read_config(path):
    if path:
        with open(path) as json_data_file:
            conf_args = json.load(json_data_file)
    else:
        raise ValueError("No config provided for classifier")

    # flatten last part of config, take either value or default as value
    for gk, gv in conf_args.items():
        for k, v in gv.items():
            conf_args[gk][k] = v["value"] if (v["value"] is not None) else v["default"]

    # DotMap for making nested dictionary accessible through dot notation
    args = DotMap(conf_args, _dynamic=False)

    return args 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def load_current_kube_credentials():
    with open(os.path.expanduser(KUBE_CONFIG_PATH), 'r') as f:
        config = DotMap(yaml.safe_load(f))

        ctx_name = config['current-context']
        ctx = next(c for c in config.contexts if c.name == ctx_name)
        cluster = next(c for c in config.clusters if c.name == ctx.context.cluster).cluster

        if 'certificate-authority' in cluster:
            ca_cert = cluster['certificate-authority']
        else:
            ca_cert = None

        if ctx.context.user:
            user = next(u for u in config.users if u.name == ctx.context.user).user
            return cluster.server, ca_cert, (user['client-certificate'], user['client-key'])
        else:
            return cluster.server, ca_cert, None 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def label_defunct(client: KubernetesAPIClient, namespace: str, job: DotMap):
    job_name = job.metadata.name
    pods = client.get('/api/v1/namespaces/{}/pods', namespace,
                      params={'labelSelector': 'job-name={}'.format(job_name)})

    defunct_ops = [{
        'op': 'add',
        'path': '/metadata/labels/defunct',
        'value': 'true'
    }]

    for pod in pods['items']:
        r = client.json_patch(defunct_ops,
                              '/api/v1/namespaces/{}/pods/{}',
                              namespace, pod.metadata.name,
                              raise_for_status=False)
        if r.status_code != 200:
            # oh well
            logger.error(
                'Failed to label pod as defunct: %s/%s',
                namespace, pod.metadata.name) 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def load_current_kube_credentials():
    with open(os.path.expanduser(KUBE_CONFIG_PATH), 'r') as kcf:
        config = DotMap(yaml.safe_load(kcf))

        ctx_name = config['current-context']
        ctx = next(c for c in config.contexts if c.name == ctx_name)
        cluster = next(c for c in config.clusters if c.name == ctx.context.cluster).cluster

        if 'certificate-authority' in cluster:
            ca_cert = cluster['certificate-authority']
        else:
            ca_cert = None

        if ctx.context.user:
            user = next(u for u in config.users if u.name == ctx.context.user).user
            return cluster.server, ca_cert, (user['client-certificate'], user['client-key'])

        return cluster.server, ca_cert, None 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def test_halton():
    carDomain = Struct({
        'position': Box([-10,10], [-10,10], [0,1]),
        'heading': Box([0, math.pi]),
        'model': DiscreteBox([0, 10])
    })

    space = FeatureSpace({
        'weather': Feature(DiscreteBox([0,12])),
        'cars': Feature(Array(carDomain, [2]))
    })

    halton_params = DotMap()
    halton_params.sample_index = -2
    halton_params.bases_skipped = 0

    sampler = FeatureSampler.haltonSamplerFor(space, halton_params)

    for i in range(3):
        print(f'Sample #{i}:')
        print(sampler.nextSample()) 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def test_feedback_multiple_lengths():
    space = FeatureSpace({
        'a': Feature(Box((0, 1)), lengthDomain=DiscreteBox((1, 2)))
    })

    def f(sample):
        assert 1 <= len(sample.a) <= 2
        return -1 if len(sample.a) == 1 and sample.a[0][0] < 0.5 else 1

    ce_params = DotMap(alpha=0.5, thres=0)
    ce_params.cont.buckets = 2
    ce_params.cont.dist = None
    ce_params.disc.dist = None
    sampler = FeatureSampler.crossEntropySamplerFor(space, ce_params)

    sampleWithFeedback(sampler, 100, f)
    l1sampler = sampler.domainSamplers[sampler.lengthDomain.makePoint(a=(1,))]
    l1dist = l1sampler.cont_sampler.dist[0]
    l2sampler = sampler.domainSamplers[sampler.lengthDomain.makePoint(a=(2,))]
    l2dists = l2sampler.cont_sampler.dist
    assert len(l1dist) == 2
    assert l1dist[0] > 0.9
    assert all(list(l2dist) == [0.5, 0.5] for l2dist in l2dists) 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def test_bayesianOptimization():
    carDomain = Struct({
        'position': Box([-10,10], [-10,10], [0,1]),
        'heading': Box([0, math.pi]),
    })

    space = FeatureSpace({
        'cars': Feature(Array(carDomain, [2]))
    })

    def f(sample):
        sample = sample.cars[0].heading[0]
        return abs(sample - 0.75)

    bo_params = DotMap()
    bo_params.init_num = 2

    sampler = FeatureSampler.bayesianOptimizationSamplerFor(space, BO_params=bo_params)

    sampleWithFeedback(sampler, 3, f) 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def read_env_cfg(config_filename = 'configs/main.cfg'):
    # Load from config file
    cfg = DotMap()

    config = cp.ConfigParser()
    config.read(config_filename)

    cfg.run_name = config.get('general_params', 'env_name')
    cfg.floorplan = str(config.get('general_params', 'floorplan'))
    cfg.o_x = float(config.get('general_params', 'o_x').split(',')[0])
    cfg.o_y = float(config.get('general_params', 'o_y').split(',')[0])
    cfg.alpha = float(config.get('general_params', 'alpha').split(',')[0])
    cfg.ceiling_z = float(config.get('general_params', 'ceiling_z').split(',')[0])
    cfg.floor_z = float(config.get('general_params', 'floor_z').split(',')[0])
    cfg.player_start_z = float(config.get('general_params', 'player_start_z').split(',')[0])

    return cfg 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def main(env, ctrl_type, ctrl_args, overrides, model_dir, logdir):
    ctrl_args = DotMap(**{key: val for (key, val) in ctrl_args})

    overrides.append(["ctrl_cfg.prop_cfg.model_init_cfg.model_dir", model_dir])
    overrides.append(["ctrl_cfg.prop_cfg.model_init_cfg.load_model", "True"])
    overrides.append(["ctrl_cfg.prop_cfg.model_pretrained", "True"])
    overrides.append(["exp_cfg.exp_cfg.ninit_rollouts", "0"])
    overrides.append(["exp_cfg.exp_cfg.ntrain_iters", "1"])
    overrides.append(["exp_cfg.log_cfg.nrecord", "1"])

    cfg = create_config(env, ctrl_type, ctrl_args, overrides, logdir)
    cfg.pprint()

    if ctrl_type == "MPC":
        cfg.exp_cfg.exp_cfg.policy = MPC(cfg.ctrl_cfg)
    exp = MBExperiment(cfg.exp_cfg)

    os.makedirs(exp.logdir)
    with open(os.path.join(exp.logdir, "config.txt"), "w") as f:
        f.write(pprint.pformat(cfg.toDict()))

    exp.run_experiment() 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def __init__(self, params):
        """Initializes an agent.

        Arguments:
            params: (DotMap) A DotMap of agent parameters.
                .env: (OpenAI gym environment) The environment for this agent.
                .noisy_actions: (bool) Indicates whether random Gaussian noise will 
                    be added to the actions of this agent.
                .noise_stddev: (float) The standard deviation to be used for the 
                    action noise if params.noisy_actions is True.
        """
        self.env = params.env
        self.noise_stddev = params.noise_stddev if params.get("noisy_actions", False) else None

        if isinstance(self.env, DotMap):
            raise ValueError("Environment must be provided to the agent at initialization.")
        if (not isinstance(self.noise_stddev, float)) and params.get("noisy_actions", False):
            raise ValueError("Must provide standard deviation for noise for noisy actions.")

        if self.noise_stddev is not None:
            self.dU = self.env.action_space.shape[0] 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def read_cfg(config_filename='configs/main.cfg', verbose=False):
    parser = ConfigParser()
    parser.optionxform = str
    parser.read(config_filename)
    cfg = DotMap()

    if verbose:
        hyphens = '-' * int((80 - len(config_filename))/2)
        print(hyphens + ' ' + config_filename + ' ' + hyphens)

    for section_name in parser.sections():
        if verbose:
            print('[' + section_name + ']')
        for name, value in parser.items(section_name):
            value = ConvertIfStringIsInt(value)
            cfg[name] = value
            spaces = ' ' * (30 - len(name))
            if verbose:
                print(name + ':' + spaces + str(cfg[name]))

    return cfg 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def resize_token_embeddings(self, new_num_tokens=None):
        # function is called as a vocab length validation inside FARM
        # fast way of returning an object with num_embeddings attribute (needed for some checks)
        # TODO add functionality to add words/tokens to a wordembeddingmodel after initialization
        temp = {}
        temp["num_embeddings"] = len(self.vocab)
        temp = DotMap(temp)
        return temp 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def is_condition_complete(condition: DotMap) -> bool:
    return condition.type == 'Complete' and str(condition.status) == 'True' 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def get_config_from_json(json_file):
    """
    Get the config from a json file
    :param json_file:
    :return: config(namespace) or config(dictionary)
    """
    # parse the configurations from the config json file provided
    with open(json_file, 'r') as config_file:
        config_dict = json.load(config_file)

    # convert the dictionary to a namespace using bunch lib
    config = DotMap(config_dict)

    return config, config_dict 

# 需要導入模塊: import dotmap [as 別名]
# 或者: from dotmap import DotMap [as 別名]
def __init__(self, baselines_params=None):
        if baselines_params is None:
            baselines_params = DotMap()
            baselines_params.alg = 'ppo2'
            baselines_params.env_id = 'MountainCar-v0'
            baseline_params.num_timesteps = 1e3
        else:
            if 'env_id' not in baseline_params or baseline_params.env_id !='MountainCar-v0':
                baseline_params.env_id = 'MountainCar-v0'
            if 'alg' not in baseline_params:
                baseline_params.alg = 'ppo2'
        super().__init__(baselines_params=baselines_params)
        if sample_type >= 1:
            self.run_task = self.run_task_retrain
        self.algs = ['ppo2', 'deepq', 'acer', 'a2c', 'trpo_mpi', 'acktr']