Python builtins.min() Examples

The following are 30 code examples of builtins.min(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module builtins , or try the search function .
Example #1
Source File: tf_util.py    From Run-Skeleton-Run with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
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Example #2
Source File: tf_util.py    From multiagent-gail with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

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# Modules
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Example #3
Source File: tf_util.py    From rl-teacher with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
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Example #4
Source File: tf_util.py    From rl-attack with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
# ================================================================
Example #5
Source File: tf_util.py    From deeprl-baselines with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
# ================================================================
Example #6
Source File: tf_util.py    From action-branching-agents with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
# ================================================================
Example #7
Source File: tf_util.py    From rl-attack-detection with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
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Example #8
Source File: tf_util.py    From gail-tf with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
# ================================================================
Example #9
Source File: tf_util.py    From emdqn with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
# ================================================================
Example #10
Source File: tf_util.py    From NoisyNet-DQN with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
# ================================================================
Example #11
Source File: tf_util.py    From ape-x with Apache License 2.0 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
# ================================================================
Example #12
Source File: tf_util.py    From BackpropThroughTheVoidRL with MIT License 6 votes vote down vote up 
def __call__(self, *inputvals):
        assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)
        nondata_vals = inputvals[0:len(self.nondata_inputs)]
        data_vals = inputvals[len(self.nondata_inputs):]
        feed_dict = dict(zip(self.nondata_inputs, nondata_vals))
        n = data_vals[0].shape[0]
        for v in data_vals[1:]:
            assert v.shape[0] == n
        for i_start in range(0, n, self.batch_size):
            slice_vals = [v[i_start:builtins.min(i_start + self.batch_size, n)] for v in data_vals]
            for (var, val) in zip(self.data_inputs, slice_vals):
                feed_dict[var] = val
            results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)
            if i_start == 0:
                sum_results = results
            else:
                for i in range(len(results)):
                    sum_results[i] = sum_results[i] + results[i]
        for i in range(len(results)):
            sum_results[i] = sum_results[i] / n
        return sum_results

# ================================================================
# Modules
# ================================================================
Example #13
Source File: tf_util.py    From emdqn with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keep_dims=keepdims)
Example #14
Source File: new_min_max.py    From V1EngineeringInc-Docs with Creative Commons Attribution Share Alike 4.0 International 5 votes vote down vote up 
def newmin(*args, **kwargs):
    return new_min_max(_builtin_min, *args, **kwargs)
Example #15
Source File: min.py    From pyramda with MIT License 5 votes vote down vote up 
def min(xs):
    return builtins.min(xs)
Example #16
Source File: new_min_max.py    From Tautulli with GNU General Public License v3.0 5 votes vote down vote up 
def newmin(*args, **kwargs):
    return new_min_max(_builtin_min, *args, **kwargs)
Example #17
Source File: tf_util.py    From deeprl-baselines with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keep_dims=keepdims)
Example #18
Source File: tf_util.py    From BackpropThroughTheVoidRL with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keep_dims=keepdims)
Example #19
Source File: fypp.py    From fypp with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def _process_arguments(self, args, keywords):
        kwdict = dict(keywords)
        argdict = {}
        nargs = min(len(args), len(self._argnames))
        for iarg in range(nargs):
            argdict[self._argnames[iarg]] = args[iarg]
        if nargs < len(args):
            if self._varpos is None:
                msg = "macro '{0}' called with too many positional arguments "\
                      "(expected: {1}, received: {2})"\
                      .format(self._name, len(self._argnames), len(args))
                raise FyppFatalError(msg, self._fname, self._spans[0])
            else:
                argdict[self._varpos] = list(args[nargs:])
        elif self._varpos is not None:
            argdict[self._varpos] = []
        for argname in self._argnames[:nargs]:
            if argname in kwdict:
                msg = "got multiple values for argument '{0}'".format(argname)
                raise FyppFatalError(msg, self._fname, self._spans[0])
        if nargs < len(self._argnames):
            for argname in self._argnames[nargs:]:
                if argname in kwdict:
                    argdict[argname] = kwdict.pop(argname)
                elif argname in self._defaults:
                    argdict[argname] = self._defaults[argname]
                else:
                    msg = "macro '{0}' called without mandatory positional "\
                          "argument '{1}'".format(self._name, argname)
                    raise FyppFatalError(msg, self._fname, self._spans[0])
        if kwdict and self._varkw is None:
            kwstr = "', '".join(kwdict.keys())
            msg = "macro '{0}' called with unknown keyword argument(s) '{1}'"\
                  .format(self._name, kwstr)
            raise FyppFatalError(msg, self._fname, self._spans[0])
        if self._varkw is not None:
            argdict[self._varkw] = kwdict
        return argdict
Example #20
Source File: stats.py    From Turing with MIT License 5 votes vote down vote up 
def min_index(args):
    return args.index(builtins.min(args))
Example #21
Source File: stats.py    From Turing with MIT License 5 votes vote down vote up 
def min(args):
    return builtins.min(args)
Example #22
Source File: sanity.py    From reframe with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def min(*args):
    '''Replacement for the built-in :func:`min() <python:min>` function.'''
    return builtins.min(*args)
Example #23
Source File: tf_util.py    From multiagent-gail with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keep_dims=keepdims)
Example #24
Source File: tf_util.py    From rl-teacher with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keep_dims=keepdims)
Example #25
Source File: cluster_monitor.py    From KubeOperator with Apache License 2.0 5 votes vote down vote up 
def sync_node_time(cluster):
    hosts = C_Host.objects.filter(
        Q(project_id=cluster.id) & ~Q(name='localhost') & ~Q(name='127.0.0.1') & ~Q(name='::1'))
    data = []
    times = []
    result = {
        'success': True,
        'data': []
    }
    for host in hosts:
        ssh_config = SshConfig(host=host.ip, port=host.port, username=host.username, password=host.password,
                               private_key=None)

        ssh_client = SSHClient(ssh_config)
        res = ssh_client.run_cmd('date')
        gmt_date = res[0]
        GMT_FORMAT = '%a %b %d %H:%M:%S CST %Y'
        date = time.strptime(gmt_date, GMT_FORMAT)
        timeStamp = int(time.mktime(date))
        times.append(timeStamp)
        show_time = time.strftime('%Y-%m-%d %H:%M:%S', date)
        time_data = {
            'hostname': host.name,
            'date': show_time,
        }
        data.append(time_data)
    result['data'] = data
    max = builtins.max(times)
    min = builtins.min(times)
    # 如果最大值减最小值超过5分钟 则判断有错
    if (max - min) > 300000:
        result['success'] = False
    return result
Example #26
Source File: tf_util.py    From rl-attack with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keep_dims=keepdims)
Example #27
Source File: new_min_max.py    From addon with GNU General Public License v3.0 5 votes vote down vote up 
def newmin(*args, **kwargs):
    return new_min_max(_builtin_min, *args, **kwargs)
Example #28
Source File: tf_util.py    From action-branching-agents with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keepdims=keepdims)
Example #29
Source File: tf_util.py    From Run-Skeleton-Run with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keep_dims=keepdims)
Example #30
Source File: tf_util.py    From rl-attack-detection with MIT License 5 votes vote down vote up 
def min(x, axis=None, keepdims=False):
    axis = None if axis is None else [axis]
    return tf.reduce_min(x, axis=axis, keep_dims=keepdims)