Python moves.reduce方法代碼示例

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def osf_crawl(k, *pths, **kw):
    '''
    osf_crawl(k) crawls the osf repository k and returns a lazy nested map structure of the
      repository's files. Folders have values that are maps of their contents while files have
      values that are their download links.
    osf_crawl(k1, k2, ...) is equivalent to osf_crawl(posixpath.join(k1, k2...)).

    The optional named argument base (default: 'osfstorage') may be specified to search in a
    non-standard storage position in the OSF URL; e.g. in the github storage.
    '''
    from six.moves import reduce
    base = kw.pop('base', 'osfstorage')
    root = kw.pop('root', None)
    if len(kw) > 0: raise ValueError('Unknown optional parameters: %s' % (list(kw.keys()),))
    if k.lower().startswith('osf:'): k = k[4:]
    k = k.lstrip('/')
    pths = [p.lstrip('/') for p in (k.split('/') + list(pths))]
    (bpth, pths) = (pths[0].strip('/'), [p for p in pths[1:] if p != ''])
    if root is None: root = _osf_tree(bpth, base=base)
    return reduce(lambda m,k: m[k], pths, root) 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def build_gemm_call(self):

        return reduce(str.__add__, (
            self.declare_NS,
            self.check_xyz_rank2,
            self.setup_z_Nz_Sz,
            self.check_xyz_double_or_float,
            self.check_ab_double_or_float,
            self.check_dims,
            self.check_strides,
            self.encode_strides_in_unit,
            self.compute_strides,
            self.begin_switch_typenum,
            self.case_float,
            self.case_float_ab_constants,
            self.case_float_gemm,
            self.case_double,
            self.case_double_ab_constants,
            self.case_double_gemm,
            self.end_switch_typenum), '') 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def values_eq_approx_high_tol(a, b):
    """
    This fct is needed to don't have DebugMode raise useless
    error due to ronding error.

    This happen as We reduce on the two last dimensions, so this
    can raise the absolute error if the number of element we
    reduce on is significant.

    """
    assert a.ndim == 4
    atol = None
    if a.shape[-1] * a.shape[-2] > 100:
        # For float32 the default atol is 1e-5
        atol = 3e-5
    return CudaNdarrayType.values_eq_approx(a, b, atol=atol) 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def get_gateway_address(self):
        """
        Perform several set of commands to obtain the gateway address
            * `adb getprop dhcp.wlan0.gateway`
            * `adb shell netcfg | grep wlan0`

        Returns:
            None if no gateway address has been found, otherwise return the gateway address

        """
        ip2int = lambda ip: reduce(lambda a, b: (a << 8) + b, map(int, ip.split('.')), 0)
        int2ip = lambda n: '.'.join([str(n >> (i << 3) & 0xFF) for i in range(0, 4)[::-1]])
        try:
            res = self.shell('getprop dhcp.wlan0.gateway')
        except AdbShellError:
            res = ''
        matcher = IP_PATTERN.search(res)
        if matcher:
            return matcher.group(0)
        ip = self.get_ip_address()
        if not ip:
            return None
        mask_len = self._get_subnet_mask_len()
        gateway = (ip2int(ip) & (((1 << mask_len) - 1) << (32 - mask_len))) + 1
        return int2ip(gateway) 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def __mul__(self, other):
        """
        Outer product of two cycles (`itertools.product`) or integer
        multiplication.

        Parameters
        ----------
        other : Cycler or int
           The second Cycler or integer
        """
        if isinstance(other, Cycler):
            return Cycler(self, other, product)
        elif isinstance(other, int):
            trans = self.by_key()
            return reduce(add, (_cycler(k, v*other)
                                for k, v in six.iteritems(trans)))
        else:
            return NotImplemented 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def simplify(self):
        """Simplify the Cycler

        Returned as a composition using only sums (no multiplications)

        Returns
        -------
        simple : Cycler
            An equivalent cycler using only summation"""
        # TODO: sort out if it is worth the effort to make sure this is
        # balanced.  Currently it is is
        # (((a + b) + c) + d) vs
        # ((a + b) + (c + d))
        # I would believe that there is some performance implications

        trans = self.by_key()
        return reduce(add, (_cycler(k, v) for k, v in six.iteritems(trans))) 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def _parse_reduction(self, operation, inames, red_exprs,
            allow_simultaneous=False):
        if isinstance(inames, p.Variable):
            inames = (inames,)

        if not isinstance(inames, (tuple)):
            raise TypeError("iname argument to reduce() must be a symbol "
                    "or a tuple of symbols")

        processed_inames = []
        for iname in inames:
            if not isinstance(iname, p.Variable):
                raise TypeError("iname argument to reduce() must be a symbol "
                        "or a tuple or a tuple of symbols")

            processed_inames.append(iname.name)

        if len(red_exprs) == 1:
            red_exprs = red_exprs[0]

        return Reduction(operation, tuple(processed_inames), red_exprs,
                allow_simultaneous=allow_simultaneous) 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def _handle_license_list(self, lics_set, cls=None):
        """
        Return a license representing a `cls` object (LicenseConjunction
        or LicenseDisjunction) from a list of license resources or None.
        """
        licenses = []
        for _, _, lics_member in self.graph.triples(
            (lics_set, self.spdx_namespace['member'], None)):
            try:
                licenses.append(self.handle_lics(lics_member))
            except CardinalityError:
                self.value_error('LICS_LIST_MEMBER', lics_member)
                break
        if len(licenses) > 1:
            return reduce(lambda a, b: cls(a, b), licenses)
        else:
            self.value_error('PKG_CONC_LIST', '')
            return 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def crt(ak, nk):
  from six.moves import reduce
  """
  Chinese-Reminders-Theorem Implementation
  using Gauss's proof and generalization on gcd(n1, n2) != 1
  Should be len(ak) == len(nk)
  Original: https://gist.github.com/elliptic-shiho/901d223135965308a5f9ff0cf99dd7c8
  Explanation: http://elliptic-shiho.hatenablog.com/entry/2016/04/03/020117

  Args:
    ak: A Numbers [a1, a2, ..., ak]
    nk: A Modulus [n1, n2, ..., nk]
  """
  assert len(ak) == len(nk)
  N = reduce(lambda x, y: x * y, nk, 1)
  l = lcm(*nk)
  s = 0
  for n, a in zip(nk, ak):
    m = N // n
    g, x, y = egcd(m, n)
    s += (m // g) * x * a
    s %= l
  return s 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def get_queryset(self):
        if not (self.request.user.is_authenticated and self.request.user.is_staff):
            raise Http404

        queryset = Plan.objects.exclude(enabled=False)

        if self.q:
            queryset = queryset.annotate(
                name_provider__name__company=Concat(
                    F("name"), Value(" "), F("provider__name"), Value(" "), F("provider__company")
                )
            )
            terms = self.q.split()

            query = reduce(
                operator.and_,
                (Q(name_provider__name__company__icontains=term) for term in terms)
            )

            queryset = queryset.filter(query)

        return queryset 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def get_common_course_modes(course_runs):
    """
    Fake implementation returning common course modes.

    Arguments:
        course_run_ids(Iterable[str]): Target Course run IDs.

    Returns:
        set: course modes found in all given course runs
    """
    course_run_modes = [
        set(seat.get("type") for seat in course_run.get("seats"))
        for course_run in FAKE_COURSE_RUNS_RESPONSE
        if course_run.get("key") in course_runs
    ]

    return six_reduce(lambda left, right: left & right, course_run_modes) 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def discriminator(self, name, image, label, feat_id):
        if name in self.reuse.keys():
            reuse = self.reuse[name]
        else:
            self.reuse[name] = True
            reuse = False

        X1 = image / 127.5 - 1
        label_concat_list = [tf.tile(tf.expand_dims(tf.expand_dims(tf.expand_dims(label[:,j],-1),-1),-1), [1,self.height,self.width,1]) for j in range(self.n_feat)]
        X2 = tf.concat(label_concat_list, -1)
        X = tf.concat([X1, X2], -1)

        with tf.variable_scope(name, reuse=reuse) as scope:
            X = self.make_conv('conv1', X, shape=[4,4,3+self.n_feat,128], strides=[1,2,2,1])
            X = self.leakyRelu(X, 0.2)
            # print(name, X.get_shape())

            X = self.make_conv_bn('conv2', X, shape=[4,4,128,256], strides=[1,2,2,1])
            X = self.leakyRelu(X, 0.2)
            # print(name, X.get_shape())

            X = self.make_conv_bn('conv3', X, shape=[4,4,256,512], strides=[1,2,2,1])
            X = self.leakyRelu(X, 0.2)
            # print(name, X.get_shape())

            X = self.make_conv_bn('conv4', X, shape=[4,4,512,512], strides=[1,2,2,1])
            X = self.leakyRelu(X, 0.2)
            # print(name, X.get_shape())

            flat_dim = reduce(lambda x,y: x*y, X.get_shape().as_list()[1:])
            X = tf.reshape(X, [-1, flat_dim])
            X = self.make_fc('fct', X, self.n_feat)
            # X = tf.nn.sigmoid(X)
            return X[:,feat_id] 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def apply(self, fgraph):
        op = self.local_opt.op_key()
        if isinstance(op, (list, tuple)):
            q = reduce(list.__iadd__, map(fgraph.get_nodes, op))
        else:
            q = list(fgraph.get_nodes(op))

        def importer(node):
            if node is not current_node:
                if node.op == op:
                    q.append(node)

        def pruner(node):
            if node is not current_node and node.op == op:
                try:
                    q.remove(node)
                except ValueError:
                    pass
        u = self.attach_updater(fgraph, importer, pruner)
        try:
            while q:
                node = q.pop()
                current_node = node
                self.process_node(fgraph, node)
        except Exception:
            self.detach_updater(fgraph, u)
            raise
        self.detach_updater(fgraph, u) 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def check_chain(r, *chain):
    """
    WRITEME

    """
    if isinstance(r, graph.Apply):
        r = r.outputs[0]
    return _check_chain(r, reduce(list.__iadd__, ([x, 0] for x in chain))) 

# 需要導入模塊: from six import moves [as 別名]
# 或者: from six.moves import reduce [as 別名]
def backtrack(self, node, tasks):
            candidates = self.fetch_tracks(node.op)
            tracks = []
            def filter(node, depth):
                new_candidates = []
                for candidate in candidates:
                    track, i, lopt = candidate
                    if i < depth:
                        pass
                    elif track[i-depth] in (None, node.op):
                        if i == depth:
                            tasks[node].append(lopt)
                        else:
                            tracks.append(candidate)
                    else:
                        new_candidates.append(candidate)
                return new_candidates
            depth = 0
            nodes = [node]
            while candidates:
                for node in nodes:
                    candidates = list(filter(node, depth))
                depth += 1
                _nodes = nodes
                nodes = reduce(list.__iadd__,
                               [reduce(list.__iadd__,
                                       [[n for n, i in out.clients if not isinstance(n, string_types)] for out in node.outputs],
                                       []) for node in nodes],
                               [])
                candidates = tracks
                tracks = []