Python numpy.cumsum方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def active_net_list(self):
        net_list = [["input", 0, 0]]
        active_cnt = np.arange(self.net_info.input_num + self.net_info.node_num + self.net_info.out_num)
        active_cnt[self.net_info.input_num:] = np.cumsum(self.is_active)

        for n, is_a in enumerate(self.is_active):
            if is_a:
                t = self.gene[n][0]
                if n < self.net_info.node_num:    # intermediate node
                    type_str = self.net_info.func_type[t]
                else:    # output node
                    type_str = self.net_info.out_type[t]

                connections = [active_cnt[self.gene[n][i+1]] for i in range(self.net_info.max_in_num)]
                net_list.append([type_str] + connections)
        return net_list


# CGP with (1 + \lambda)-ES 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def step_function(x, center=0, FWHM=0.1, height=1, check=True):
    """
    Compute a step function as the cumulative summation of a gaussian
    distribution of a given vector.

    :Parameters:
        #. x (numpy.ndarray): The vector to compute the gaussian. gaussian
           is computed as a function of x.
        #. center (number): The center of the step function which is the
           the center of the gaussian.
        #. FWHM (number): The Full Width at Half Maximum of the gaussian.
        #. height (number): The height of the step function.
        #. check (boolean): whether to check arguments before generating
           vectors.
    """
    if check:
        assert is_number(height), LOGGER.error("height must be a number")
        height = FLOAT_TYPE(height)
    g  = gaussian(x, center=center, FWHM=FWHM, normalize=False, check=check)
    sf = np.cumsum(g)
    sf /= sf[-1]
    return (sf*height).astype(FLOAT_TYPE) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def calc_pr(gt, out, wt=None):
  if wt is None:
    wt = np.ones((gt.size,1))

  gt = gt.astype(np.float64).reshape((-1,1))
  wt = wt.astype(np.float64).reshape((-1,1))
  out = out.astype(np.float64).reshape((-1,1))

  gt = gt*wt
  tog = np.concatenate([gt, wt, out], axis=1)*1.
  ind = np.argsort(tog[:,2], axis=0)[::-1]
  tog = tog[ind,:]
  cumsumsortgt = np.cumsum(tog[:,0])
  cumsumsortwt = np.cumsum(tog[:,1])
  prec = cumsumsortgt / cumsumsortwt
  rec = cumsumsortgt / np.sum(tog[:,0])

  ap = voc_ap(rec, prec)
  return ap, rec, prec 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def sample(self):
        """
        This is the core sampling method. Samples a state from a
        demonstration, in accordance with the configuration.
        """

        # chooses a sampling scheme randomly based on the mixing ratios
        seed = random.uniform(0, 1)
        ratio = np.cumsum(self.scheme_ratios)
        ratio = ratio > seed
        for i, v in enumerate(ratio):
            if v:
                break

        sample_method = getattr(self, self.sample_method_dict[self.sampling_schemes[i]])
        return sample_method() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def zipf_distribution(nbr_symbols, alpha):
  """Helper function: Create a Zipf distribution.

  Args:
    nbr_symbols: number of symbols to use in the distribution.
    alpha: float, Zipf's Law Distribution parameter. Default = 1.5.
      Usually for modelling natural text distribution is in
      the range [1.1-1.6].

  Returns:
    distr_map: list of float, Zipf's distribution over nbr_symbols.

  """
  tmp = np.power(np.arange(1, nbr_symbols + 1), -alpha)
  zeta = np.r_[0.0, np.cumsum(tmp)]
  return [x / zeta[-1] for x in zeta] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def gen_dla(
    n_dim=100, n_branch=20, branch_length=100, rand_multiplier=2, seed=37, sigma=4
):
    np.random.seed(seed)
    M = np.cumsum(-1 + rand_multiplier * np.random.rand(branch_length, n_dim), 0)
    for i in range(n_branch - 1):
        ind = np.random.randint(branch_length)
        new_branch = np.cumsum(
            -1 + rand_multiplier * np.random.rand(branch_length, n_dim), 0
        )
        M = np.concatenate([M, new_branch + M[ind, :]])

    noise = np.random.normal(0, sigma, M.shape)
    M = M + noise

    # returns the group labels for each point to make it easier to visualize
    # embeddings
    C = np.array([i // branch_length for i in range(n_branch * branch_length)])

    return M, C 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def residual_resample(weights):
    n = len(weights)
    indices = np.zeros(n, np.uint32)
    # take int(N*w) copies of each weight
    num_copies = (n * weights).astype(np.uint32)
    k = 0
    for i in range(n):
        for _ in range(num_copies[i]):  # make n copies
            indices[k] = i
            k += 1
    # use multinormial resample on the residual to fill up the rest.
    residual = weights - num_copies  # get fractional part
    residual /= np.sum(residual)
    cumsum = np.cumsum(residual)
    cumsum[-1] = 1
    indices[k:n] = np.searchsorted(cumsum, np.random.uniform(0, 1, n - k))
    return indices 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def create_indices(positions, weights):
    n = len(weights)
    indices = np.zeros(n, np.uint32)
    cumsum = np.cumsum(weights)
    i, j = 0, 0
    while i < n:
        if positions[i] < cumsum[j]:
            indices[i] = j
            i += 1
        else:
            j += 1

    return indices


### end rlabbe's resampling functions 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def unserialize(cls, data, block_size, back_window):
        uncompressed = lz4.block.decompress(data)
        nb_points = (
            len(uncompressed) // cls._SERIALIZATION_TIMESTAMP_VALUE_LEN
        )

        try:
            timestamps = numpy.frombuffer(uncompressed, dtype='<Q',
                                          count=nb_points)
            values = numpy.frombuffer(
                uncompressed, dtype='<d',
                offset=nb_points * cls._SERIALIZATION_TIMESTAMP_LEN)
        except ValueError:
            raise InvalidData

        return cls.from_data(
            numpy.cumsum(timestamps),
            values,
            block_size=block_size,
            back_window=back_window) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [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 cumsum [as 別名]
def work_balanced_partition(tasks, costs=None):
    if costs is None:
        costs = numpy.ones(tasks)
    if rank == 0:
        segsize = float(sum(costs)) / pool.size
        loads = []
        cum_costs = numpy.cumsum(costs)
        start_id = 0
        for k in range(pool.size):
            stop_id = numpy.argmin(abs(cum_costs - (k+1)*segsize)) + 1
            stop_id = max(stop_id, start_id+1)
            loads.append([start_id,stop_id])
            start_id = stop_id
        comm.bcast(loads)
    else:
        loads = comm.bcast()
    if rank < len(loads):
        start, stop = loads[rank]
        return tasks[start:stop]
    else:
        return tasks[:0] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [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 cumsum [as 別名]
def cisdvec_to_amplitudes(civec, nmo, nocc):
    norba, norbb = nmo
    nocca, noccb = nocc
    nvira = norba - nocca
    nvirb = norbb - noccb
    nooa = nocca * (nocca-1) // 2
    nvva = nvira * (nvira-1) // 2
    noob = noccb * (noccb-1) // 2
    nvvb = nvirb * (nvirb-1) // 2
    size = (1, nocca*nvira, noccb*nvirb, nocca*noccb*nvira*nvirb,
            nooa*nvva, noob*nvvb)
    loc = numpy.cumsum(size)
    c0 = civec[0]
    c1a = civec[loc[0]:loc[1]].reshape(nocca,nvira)
    c1b = civec[loc[1]:loc[2]].reshape(noccb,nvirb)
    c2ab = civec[loc[2]:loc[3]].reshape(nocca,noccb,nvira,nvirb)
    c2aa = _unpack_4fold(civec[loc[3]:loc[4]], nocca, nvira)
    c2bb = _unpack_4fold(civec[loc[4]:loc[5]], noccb, nvirb)
    return c0, (c1a,c1b), (c2aa,c2ab,c2bb) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def _ecg_findpeaks_MWA(signal, window_size):
    """From https://github.com/berndporr/py-ecg-detectors/"""

    mwa = np.zeros(len(signal))
    sums = np.cumsum(signal)

    def get_mean(begin, end):
        if begin == 0:
            return sums[end - 1] / end

        dif = sums[end - 1] - sums[begin - 1]
        return dif / (end - begin)

    for i in range(len(signal)):  # pylint: disable=C0200
        if i < window_size:
            section = signal[0:i]
        else:
            section = get_mean(i - window_size, i)

        if i != 0:
            mwa[i] = np.mean(section)
        else:
            mwa[i] = signal[i]

    return mwa 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumsum [as 別名]
def _signal_changepoints_cost_mean(signal):
    """Cost function for a normally distributed signal with a changing mean."""
    i_variance_2 = 1 / (np.var(signal) ** 2)
    cmm = [0.0]
    cmm.extend(np.cumsum(signal))

    cmm2 = [0.0]
    cmm2.extend(np.cumsum(np.abs(signal)))

    def cost(start, end):
        cmm2_diff = cmm2[end] - cmm2[start]
        cmm_diff = pow(cmm[end] - cmm[start], 2)
        i_diff = end - start
        diff = cmm2_diff - cmm_diff
        return (diff / i_diff) * i_variance_2

    return cost