Python itertools.combinations方法代碼示例

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def init_W(self, mode='normal'):

        self.W = {}

        if (self.status != 'load_train_data') and (self.status != 'train'):
            print("Please load train data first.")
            return self.W

        self.status = 'init'

        self.data_num = len(self.train_Y)
        self.data_demension = len(self.train_X[0])
        self.class_list = list(itertools.combinations(np.unique(self.train_Y), 2))

        for class_item in self.class_list:
            self.W[class_item] = np.zeros(self.data_demension)

        return self.W 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def params(self) -> Iterable[sympy.Symbol]:
        """The parameters of the ansatz."""
        for i in range(self.iterations):
            for p in range(len(self.qubits)):
                if (self.include_all_z or not
                        numpy.isclose(self.hamiltonian.one_body[p, p], 0)):
                    yield LetterWithSubscripts('U', p, i)
            for p, q in itertools.combinations(range(len(self.qubits)), 2):
                if (self.include_all_xxyy or not
                        numpy.isclose(self.hamiltonian.one_body[p, q].real, 0)):
                    yield LetterWithSubscripts('T', p, q, i)
                if (self.include_all_yxxy or not
                        numpy.isclose(self.hamiltonian.one_body[p, q].imag, 0)):
                    yield LetterWithSubscripts('W', p, q, i)
                if (self.include_all_cz or not
                        numpy.isclose(self.hamiltonian.two_body[p, q], 0)):
                    yield LetterWithSubscripts('V', p, q, i) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def from_words(cls, words, window_size=3):
        """Construct a TrigramCollocationFinder for all trigrams in the given
        sequence.
        """
        if window_size < 3:
            raise ValueError("Specify window_size at least 3")

        wfd = FreqDist()
        wildfd = FreqDist()
        bfd = FreqDist()
        tfd = FreqDist()
        for window in ngrams(words, window_size, pad_right=True):
            w1 = window[0]
            if w1 is None:
                continue
            for w2, w3 in _itertools.combinations(window[1:], 2):
                wfd[w1] += 1
                if w2 is None:
                    continue
                bfd[(w1, w2)] += 1
                if w3 is None:
                    continue
                wildfd[(w1, w3)] += 1
                tfd[(w1, w2, w3)] += 1
        return cls(wfd, bfd, wildfd, tfd) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def canInd(P, ni):
    l = len(P)
    ind = range(l)
    if ni < 2:
        return [[xx] for xx in ind]
    if ni >= l:
        return [ind]
    im = intersection_matrix(P)
    can = []
    for w in combinations(ind, ni):
        fg = True
        for i in w:
            for j in w:
                if im[i, j] == 0:
                    fg = False
                    break
            if not fg:
                break
        if fg:
            can.append(list(w))
    return can 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def get_ngrams(sentence, max_length, skip_ngrams=False, add_tags = True):
    # return ngrams of length up to max_length as found in sentence.
    out = []
    words = sentence.split()
    if add_tags :
        words = ["<s>"]+words+["</s>"]
    if not skip_ngrams :
        for i in range(len(words)):
            for n in range(1,min(max_length+1, len(words)-i+1)): 
                this_ngram = " ".join(words[i:i+n])
                out.append((this_ngram,[]))
    else :
        for n in range(1, max_length+1):
            subsets = set(itertools.combinations(range(len(words)), n))
            for subset in subsets:
                subset = sorted(subset)
                dists = [(subset[i]-subset[i-1]) for i in range(1, len(subset))]
                out.append((" ".join([words[j] for j in subset]), dists))
            
        
    return out 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def connected_combos(self, possible_nodes, size):
        """
        Computes the number of different connected subsets of `possible_nodes`
        containing `size` nodes.

        Parameters
        ----------
        possible_nodes : list
            A list of node (qubit) labels.

        size : int
            The size of the connected subsets being sought (counted).

        Returns
        -------
        int
        """
        count = 0
        for selected_nodes in _itertools.combinations(possible_nodes, size):
            if self.are_glob_connected(selected_nodes): count += 1
        return count 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def get_all_connected_sets(self, n):
        """
        Returns all connected sets of `n` qubits. Note that for a large device with
        this will be often be an unreasonably large number of sets of qubits, and so
        the run-time of this method will be unreasonable.

        Parameters
        ----------
        n: int
            The number of qubits within each set.

        Returns
        -------
        list
            All sets of `n` connected qubits.

        """
        connectedqubits = []
        for combo in _iter.combinations(self.qubit_labels, n):
            if self.qubitgraph.subgraph(list(combo)).are_glob_connected(combo):
                connectedqubits.append(combo)

        return connectedqubits

    #Note:  Below method gets all subgraphs up to full graph size. 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def find_all_sets_of_compatible_twoQgates(edgelist, n, gatename='Gcnot', aslabel=False):
    """
    todo.

    n : int . the number of two-qubit gates to have in the set.

    """
    co2Qgates = []

    # Go for all combinations of n two-qubit gates from the edgelist.
    for npairs in _itertools.combinations(edgelist, n):

        # Make a list of the qubits involved in the gates
        flat_list = [item for sublist in npairs for item in sublist]

        # If no qubit is involved in more than one gate we accept the combination
        if len(flat_list) == len(set(flat_list)):
            if aslabel:
                co2Qgates.append([_lbl.Label(gatename, pair) for pair in npairs])
            else:
                co2Qgates.append([gatename + ':' + pair[0] + ':' + pair[1] for pair in npairs])

    return co2Qgates 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def connected_combos(self, possible_indices, size):
        count = 0
        for selected_inds in _itertools.combinations(possible_indices, size):
            if self.are_connected(selected_inds): count += 1
        return count

#     def remove(self, node):
#         """ Remove all references to node """
#         for n, cxns in self._graph.iteritems():
#             try:
#                 cxns.remove(node)
#             except KeyError:
#                 pass
#         try:
#             del self._graph[node]
#         except KeyError:
#             pass 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def complete(self) -> _Completions:
        valid_values = self.valtype.valid_values
        if valid_values is None:
            return None

        out = []
        # Single value completions
        for value in valid_values:
            desc = valid_values.descriptions.get(value, "")
            out.append((json.dumps([value]), desc))

        combinables = self.combinable_values
        if combinables is None:
            combinables = list(valid_values)
        # Generate combinations of each possible value combination
        for size in range(2, len(combinables) + 1):
            for combination in itertools.combinations(combinables, size):
                out.append((json.dumps(combination), ''))
        return out 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def generate_dataset(sample_range,repo_path,num_speaker=2):
    '''
    A function to generate dataset
    :param sample_range: range of the sample to create the dataset
    :param repo_path: audio repository
    :param num_speaker: number of speaker to separate
    :return: X_data, y_data
    '''
    audio_path_list = generate_path_list(sample_range,repo_path)
    num_data = 0

    combinations = itertools.combinations(audio_path_list,num_speaker)
    for combo in combinations:
        num_data += 1
        generate_mix_sample(combo,num_speaker)

    print('number of the data generated: ',num_data) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def _LeastSimilarCoverage(files, subset):
  """Returns pair of fonts having inconsistent coverage for a subset.

  Args:
    files: List of font files
    subset: Name of subset
  Returns:
    3 tuple of (file1, file2, number of codepoints difference)
  """
  worst = (None, None, 0)
  subsetcps = fonts.CodepointsInSubset(subset, True)
  for pair in itertools.combinations(files, 2):
    inconsistency = _InconsistentSubsetSupport(pair[0], pair[1], subsetcps)
    if inconsistency > worst[2]:
      worst = (pair[0], pair[1], inconsistency)
  return worst 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def _find_skeleton(self, data, variable_types):
        """
        For each pair of nodes, run a conditional independence test over
        larger and larger conditioning sets to try to find a set that
        d-separates the pair.  If such a set exists, cut the edge between
        the nodes.  If not, keep the edge.
        """
        self.separating_sets = {}
        if not self.max_k:
            self.max_k = len(self._g.nodes) + 1
        for N in range(self.max_k + 1):
            for (x, y) in list(self._g.edges()):
                x_neighbors = list(self._g.neighbors(x))
                y_neighbors = list(self._g.neighbors(y))
                z_candidates = list(set(x_neighbors + y_neighbors) - set([x, y]))
                for z in itertools.combinations(z_candidates, N):
                    test = self.independence_test([y], [x], list(z), data, self.alpha)
                    if test.independent():
                        self._g.remove_edge(x, y)
                        self.separating_sets[(x, y)] = z
                        break 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def create_cell_generator(num_nodes):
    h_connections = [
        Bool(name='in_%d_%d' % (in_id, out_id))
        for (in_id, out_id) in itertools.combinations(range(num_nodes + 2), 2)
    ]

    cell_ops = [
        D(['conv1', 'conv3', 'max3'], name='node_%d' % i)
        for i in range(num_nodes)
    ]

    def generate(filters):
        return cell(
            lambda channels: mo.siso_sequential(
                [conv2d(D([channels]), D([1])),
                 batch_normalization(),
                 relu()]), lambda num_inputs, node_id, channels:
            intermediate_node_fn(num_inputs, node_id, channels, cell_ops),
            concat, h_connections, 5, filters)

    return generate 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import combinations [as 別名]
def test_count_nonzero_axis_consistent(self):
        # Check that the axis behaviour for valid axes in
        # non-special cases is consistent (and therefore
        # correct) by checking it against an integer array
        # that is then casted to the generic object dtype
        from itertools import combinations, permutations

        axis = (0, 1, 2, 3)
        size = (5, 5, 5, 5)
        msg = "Mismatch for axis: %s"

        rng = np.random.RandomState(1234)
        m = rng.randint(-100, 100, size=size)
        n = m.astype(object)

        for length in range(len(axis)):
            for combo in combinations(axis, length):
                for perm in permutations(combo):
                    assert_equal(
                        np.count_nonzero(m, axis=perm),
                        np.count_nonzero(n, axis=perm),
                        err_msg=msg % (perm,))