Python numpy.split方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def KLDivergenceLoss():
    '''KLDivergenceLoss loss
    '''

    data = mx.sym.Variable('data')
    mu1, lv1 = mx.sym.split(data,  num_outputs=2, axis=0)
    mu2 = mx.sym.zeros_like(mu1)
    lv2 = mx.sym.zeros_like(lv1)

    v1 = mx.sym.exp(lv1)
    v2 = mx.sym.exp(lv2)
    mu_diff_sq = mx.sym.square(mu1 - mu2)
    dimwise_kld = .5 * (
    (lv2 - lv1) + mx.symbol.broadcast_div(v1, v2) + mx.symbol.broadcast_div(mu_diff_sq, v2) - 1.)
    KL = mx.symbol.sum(dimwise_kld, axis=1)

    KLloss = mx.symbol.MakeLoss(mx.symbol.mean(KL),name='KLloss')
    return KLloss 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def intersection(boxes1, boxes2):
  """Compute pairwise intersection areas between boxes.

  Args:
    boxes1: a numpy array with shape [N, 4] holding N boxes
    boxes2: a numpy array with shape [M, 4] holding M boxes

  Returns:
    a numpy array with shape [N*M] representing pairwise intersection area
  """
  [y_min1, x_min1, y_max1, x_max1] = np.split(boxes1, 4, axis=1)
  [y_min2, x_min2, y_max2, x_max2] = np.split(boxes2, 4, axis=1)

  all_pairs_min_ymax = np.minimum(y_max1, np.transpose(y_max2))
  all_pairs_max_ymin = np.maximum(y_min1, np.transpose(y_min2))
  intersect_heights = np.maximum(
      np.zeros(all_pairs_max_ymin.shape),
      all_pairs_min_ymax - all_pairs_max_ymin)
  all_pairs_min_xmax = np.minimum(x_max1, np.transpose(x_max2))
  all_pairs_max_xmin = np.maximum(x_min1, np.transpose(x_min2))
  intersect_widths = np.maximum(
      np.zeros(all_pairs_max_xmin.shape),
      all_pairs_min_xmax - all_pairs_max_xmin)
  return intersect_heights * intersect_widths 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def test_fragsep_error():

    with pytest.raises(qcelemental.ValidationError) as e:
        qcelemental.molparse.from_arrays(
            domain="qmvz",
            speclabel=True,
            elbl=["ar1", "42AR2"],
            fragment_multiplicities=[3, 3],
            fragment_separators=np.array(["1"]),
            geom_unsettled=[[], ["1", "bond"]],
            hint_types=[],
            units="Bohr",
            variables=[("bond", "3")],
        )

    assert "unable to perform trial np.split on geometry" in str(e.value) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def _split_into_xyxy(self):
        if self.mode == "xyxy":
            # xmin, ymin, xmax, ymax = self.bbox.split(1, dim=-1)
            xmin, ymin, xmax, ymax = np.split(self.bbox, 4, axis=1)
            return xmin, ymin, xmax, ymax
        elif self.mode == "xywh":
            TO_REMOVE = 1
            xmin, ymin, w, h = np.split(self.bbox, 4, axis=1)
            return (
                xmin,
                ymin,
                # xmin + (w - TO_REMOVE).clamp(min=0),
                # ymin + (h - TO_REMOVE).clamp(min=0),
                xmin + np.clip(w - TO_REMOVE, 0, None),
                ymin + np.clip(h - TO_REMOVE, 0, None),
            )
        else:
            raise RuntimeError("Should not be here")

    # def resize(self, size, *args, **kwargs): 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def vector_to_amplitudes(vec, nmo, nocc, nkpts=1):
    nocca, noccb = nocc
    nmoa, nmob = nmo
    nvira, nvirb = nmoa - nocca, nmob - noccb
    sizes = (nkpts*nocca*nvira, nkpts*noccb*nvirb,
             nkpts**3*nocca**2*nvira**2, nkpts**3*nocca*noccb*nvira*nvirb,
             nkpts**3*noccb**2*nvirb**2)
    sections = np.cumsum(sizes[:-1])
    t1a, t1b, t2aa, t2ab, t2bb = np.split(vec, sections)

    t1a = t1a.reshape(nkpts,nocca,nvira)
    t1b = t1b.reshape(nkpts,noccb,nvirb)
    t2aa = t2aa.reshape(nkpts,nkpts,nkpts,nocca,nocca,nvira,nvira)
    t2ab = t2ab.reshape(nkpts,nkpts,nkpts,nocca,noccb,nvira,nvirb)
    t2bb = t2bb.reshape(nkpts,nkpts,nkpts,noccb,noccb,nvirb,nvirb)
    return (t1a,t1b), (t2aa,t2ab,t2bb) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def vector_to_amplitudes(vector, nmo, nocc):
    nocca, noccb = nocc
    nmoa, nmob = nmo
    nvira, nvirb = nmoa-nocca, nmob-noccb
    nocc = nocca + noccb
    nvir = nvira + nvirb
    nov = nocc * nvir
    size = nov + nocc*(nocc-1)//2*nvir*(nvir-1)//2
    if vector.size == size:
        #return ccsd.vector_to_amplitudes_s4(vector, nmo, nocc)
        raise RuntimeError('Input vector is GCCSD vecotr')
    else:
        sizea = nocca * nvira + nocca*(nocca-1)//2*nvira*(nvira-1)//2
        sizeb = noccb * nvirb + noccb*(noccb-1)//2*nvirb*(nvirb-1)//2
        sections = np.cumsum([sizea, sizeb])
        veca, vecb, t2ab = np.split(vector, sections)
        t1a, t2aa = ccsd.vector_to_amplitudes_s4(veca, nmoa, nocca)
        t1b, t2bb = ccsd.vector_to_amplitudes_s4(vecb, nmob, noccb)
        t2ab = t2ab.copy().reshape(nocca,noccb,nvira,nvirb)
        return (t1a,t1b), (t2aa,t2ab,t2bb) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def np_sample(img, coords):
    # a numpy implementation of ImageSample layer
    coords = np.maximum(coords, 0)
    coords = np.minimum(coords, np.array([img.shape[0] - 1, img.shape[1] - 1]))

    lcoor = np.floor(coords).astype('int32')
    ucoor = lcoor + 1
    ucoor = np.minimum(ucoor, np.array([img.shape[0] - 1, img.shape[1] - 1]))
    diff = coords - lcoor
    neg_diff = 1.0 - diff

    lcoory, lcoorx = np.split(lcoor, 2, axis=2)
    ucoory, ucoorx = np.split(ucoor, 2, axis=2)
    diff = np.repeat(diff, 3, 2).reshape((diff.shape[0], diff.shape[1], 2, 3))
    neg_diff = np.repeat(neg_diff, 3, 2).reshape((diff.shape[0], diff.shape[1], 2, 3))
    diffy, diffx = np.split(diff, 2, axis=2)
    ndiffy, ndiffx = np.split(neg_diff, 2, axis=2)

    ret = img[lcoory, lcoorx, :] * ndiffx * ndiffy + \
        img[ucoory, ucoorx, :] * diffx * diffy + \
        img[lcoory, ucoorx, :] * ndiffy * diffx + \
        img[ucoory, lcoorx, :] * diffy * ndiffx
    return ret[:, :, 0, :] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def dmc_propagate_parallel(wf,configs,weights,client, npartitions, *args, **kwargs):
    config = configs.split(npartitions)
    weight = np.split(weights,npartitions)
    runs=[ client.submit(dmc_propagate, wf, conf , wt, *args, **kwargs) for conf,wt in zip(config, weight)]
    allresults = list(zip(*[r.result() for r in runs]))
    configs.join(allresults[1])
    weights = np.concatenate(allresults[2])
    confweight = np.array([len(c.configs) for c in config], dtype=float)
    confweight_avg = confweight/(np.mean(confweight)*npartitions)
    weight = np.array([w['weight'] for w in allresults[0]])
    weight_avg = weight/np.mean(weight)
    block_avg = {}
    for k in allresults[0][0].keys():
        block_avg[k] = np.sum([res[k]*ww*cw for res,cw,ww in zip(allresults[0],confweight_avg, weight_avg)], axis=0)
    block_avg['weight'] = np.mean(weight)
    return block_avg, configs, weights 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def mini_batch(self, data_file):
		token_seqs = []
		with codecs.open(data_file, 'r', encoding='utf-8') as f:
			for line in f:
				line = line.strip('\n')
				parse_line = list(map(int, line.split()))
				if pm.REAL_WORLD_DATA:
					if len(parse_line) == pm.WGAN_SEQ_LENGTH:
						token_seqs.append(parse_line)
				else:
					if len(parse_line) == pm.SEQ_LENGTH:
						token_seqs.append(parse_line)

		self.num_batch = int(len(token_seqs) / self.batch_size)
		token_seqs = token_seqs[:self.num_batch * self.batch_size]
		self.token_sentences = np.array(token_seqs)
		self.sequence_batch = np.split(self.token_sentences, self.num_batch, 0)
		self.reset_pointer() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def build_vocabulary(self, path, datafile, vocab_size, char=True):
		files = codecs.open(datafile, 'r', encoding='utf-8').read()
		if char:
			words = []
			files = files.split('\n')
			for word in files:
				word = tuple(word)
				words.append(word)
		else:
			words = files.split()
		wordcount = Counter(c for line in words for c in line if c != ' ')
		with codecs.open(path, 'w', encoding='utf-8') as f:
			f.write("{}\t1000000000\n{}\t1000000000\n{}\t1000000000\n{}\t1000000000\n{}\t1000000000\n".format("<PAD>", "<UNK>", "<SOS>", "<EOS>", "<SPA>"))
			for word, count in wordcount.most_common(len(wordcount)-5):
				f.write("{}\t{}\n".format(word, count))
		self.vocab_size = len(wordcount) - 5 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def backpropagate(self, delta) -> np.ndarray:
        shape = delta.shape
        delta = rtm(delta)

        h, t, c = np.split(self.gates, 3, axis=1)

        dh = self.activation.backward(h) * t * delta
        dt = sigmoid.backward(t) * h * delta
        dc = sigmoid.backward(c) * self.inputs * delta
        dx = c * delta

        dgates = np.concatenate((dh, dt, dc), axis=1)
        self.nabla_w = self.inputs.T.dot(dgates)
        self.nabla_b = dgates.sum(axis=0)

        return (dgates.dot(self.weights.T) + dx).reshape(shape) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def clip_boxes_graph(boxes, window):
    """
    boxes: [N, (y1, x1, y2, x2)]
    window: [4] in the form y1, x1, y2, x2
    """
    # Split
    wy1, wx1, wy2, wx2 = tf.split(window, 4)
    y1, x1, y2, x2 = tf.split(boxes, 4, axis=1)
    # Clip
    y1 = tf.maximum(tf.minimum(y1, wy2), wy1)
    x1 = tf.maximum(tf.minimum(x1, wx2), wx1)
    y2 = tf.maximum(tf.minimum(y2, wy2), wy1)
    x2 = tf.maximum(tf.minimum(x2, wx2), wx1)
    clipped = tf.concat([y1, x1, y2, x2], axis=1, name="clipped_boxes")
    clipped.set_shape((clipped.shape[0], 4))
    return clipped 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def test_append_read_large_ndarray(library, fw_pointers_cfg):
    with FwPointersCtx(fw_pointers_cfg):
        dtype = np.dtype([('abc', 'int64')])
        ndarr = np.arange(50 * 1024 * 1024 / dtype.itemsize).view(dtype=dtype)
        assert len(ndarr.tostring()) > 16 * 1024 * 1024
        library.write('MYARR1', ndarr)
        # Exactly enough appends to trigger 2 re-compacts, so the result should be identical
        # to writing the whole array at once
        ndarr2 = np.arange(240).view(dtype=dtype)
        for n in np.split(ndarr2, 120):
            library.append('MYARR1', n)

        saved_arr = library.read('MYARR1').data
        assert np.all(np.concatenate([ndarr, ndarr2]) == saved_arr)

        library.write('MYARR2', np.concatenate([ndarr, ndarr2]))

        version1 = library._read_metadata('MYARR1')
        version2 = library._read_metadata('MYARR2')
        assert version1['append_count'] == version2['append_count']
        assert version1['append_size'] == version2['append_size']
        assert version1['segment_count'] == version2['segment_count']
        assert version1['up_to'] == version2['up_to'] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def forward(self, x, sequence_mask):
        x = self.c_attn(x)
        query, key, value = x.split(self.split_size, dim=2)
        query = self.split_heads(query)
        key = self.split_heads(key, k=True)
        value = self.split_heads(value)
        a = self._attn(query, key, value, sequence_mask)
        a = self.merge_heads(a)
        a = self.c_proj(a)
        a = self.resid_dropout(a)
        return a 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import split [as 別名]
def read_data(file_path):
    with open(file_path, "r") as fin:
        # 將整個文檔讀進一個長字符串
        id_string = ' '.joint([line.strip() for line in fin.readlines()])
    id_list = [int(w) for w in id_string.split()]
    return id_list