Python functools.reduce() Examples

The following are 30 code examples of functools.reduce(). 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 functools , or try the search function .
Example #1
Source Project: vergeml   Author: mme   File: loader.py    License: MIT License 6 votes vote down vote up
def begin_read_samples(self):
        if self.cache:
            return

        self.input.begin_read_samples()
         # copy meta
        if self.output:
            self.output.meta = self.input.meta

        self.multipliers = {}
        self.rngs = {}

        def _mul(split):
            return reduce(operator.mul, map(lambda op: _get_multiplier(split, op), self.ops), 1)

        for split in SPLITS:
            self.multipliers[split] = _mul(split)
            self.cache[split] = self._calculate_num_samples(split)
            self.rngs[split] = self.cache[split] * [None]

        self.input.end_read_samples() 
Example #2
Source Project: xrft   Author: xgcm   File: xrft.py    License: MIT License 6 votes vote down vote up
def _apply_window(da, dims, window_type='hanning'):
    """Creating windows in dimensions dims."""

    if window_type not in ['hanning']:
        raise NotImplementedError("Only hanning window is supported for now.")

    numpy_win_func = getattr(np, window_type)

    if da.chunks:
        def dask_win_func(n):
            return dsar.from_delayed(
                delayed(numpy_win_func, pure=True)(n),
                (n,), float)
        win_func = dask_win_func
    else:
        win_func = numpy_win_func

    windows = [xr.DataArray(win_func(len(da[d])),
               dims=da[d].dims, coords=da[d].coords) for d in dims]

    return da * reduce(operator.mul, windows[::-1]) 
Example #3
Source Project: models   Author: kipoi   File: convert_Basset_to_pytorch.py    License: MIT License 6 votes vote down vote up
def simplify_source(s):
    s = map(lambda x: x.replace(',(1, 1),(0, 0),1,1,bias=True),#Conv2d',')'),s)
    s = map(lambda x: x.replace(',(0, 0),1,1,bias=True),#Conv2d',')'),s)
    s = map(lambda x: x.replace(',1,1,bias=True),#Conv2d',')'),s)
    s = map(lambda x: x.replace(',bias=True),#Conv2d',')'),s)
    s = map(lambda x: x.replace('),#Conv2d',')'),s)
    s = map(lambda x: x.replace(',1e-05,0.1,True),#BatchNorm2d',')'),s)
    s = map(lambda x: x.replace('),#BatchNorm2d',')'),s)
    s = map(lambda x: x.replace(',(0, 0),ceil_mode=False),#MaxPool2d',')'),s)
    s = map(lambda x: x.replace(',ceil_mode=False),#MaxPool2d',')'),s)
    s = map(lambda x: x.replace('),#MaxPool2d',')'),s)
    s = map(lambda x: x.replace(',(0, 0),ceil_mode=False),#AvgPool2d',')'),s)
    s = map(lambda x: x.replace(',ceil_mode=False),#AvgPool2d',')'),s)
    s = map(lambda x: x.replace(',bias=True)),#Linear',')), # Linear'),s)
    s = map(lambda x: x.replace(')),#Linear',')), # Linear'),s)

    s = map(lambda x: '{},\n'.format(x),s)
    s = map(lambda x: x[1:],s)
    s = reduce(lambda x,y: x+y, s)
    return s 
Example #4
Source Project: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def row_norms(ii, f=Ellipsis, squared=False):
    '''
    row_norms(ii) yields a potential function h(x) that calculates the vector norms of the rows of
      the matrix formed by [x[i] for i in ii] (ii is a matrix of parameter indices).
    row_norms(ii, f) yield a potential function h(x) equivalent to compose(row_norms(ii), f).
    '''
    try:
        (n,m) = ii
        # matrix shape given
        ii = np.reshape(np.arange(n*m), (n,m))
    except Exception: ii = np.asarray(ii)
    f = to_potential(f)
    if is_const_potential(f):
        q = flattest(f.c)
        q = np.sum([q[i]**2 for i in ii.T], axis=0)
        return PotentialConstant(q if squared else np.sqrt(q))
    F = reduce(lambda a,b: a + b, [part(Ellipsis, col)**2 for col in ii.T])
    F = compose(F, f)
    if not squared: F = sqrt(F)
    return F 
Example #5
Source Project: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def col_norms(ii, f=Ellipsis, squared=False):
    '''
    col_norms(ii) yields a potential function h(x) that calculates the vector norms of the columns
      of the matrix formed by [x[i] for i in ii] (ii is a matrix of parameter indices).
    col_norms(ii, f) yield a potential function h(x) equivalent to compose(col_norms(ii), f).
    '''
    try:
        (n,m) = ii
        # matrix shape given
        ii = np.reshape(np.arange(n*m), (n,m))
    except Exception: ii = np.asarray(ii)
    f = to_potential(f)
    if is_const_potential(f):
        q = flattest(f.c)
        q = np.sum([q[i]**2 for i in ii], axis=0)
        return PotentialConstant(q if squared else np.sqrt(q))
    F = reduce(lambda a,b: a + b, [part(Ellipsis, col)**2 for col in ii])
    F = compose(F, f)
    if not squared: F = sqrt(F)
    return F 
Example #6
Source Project: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def cplus(*args):
    '''
    cplus(a, b...) returns the sum of all the values as a numpy array object. Like numpy's add
      function or a+b syntax, plus will thread over the latest dimension possible.

    Additionally, cplus works correctly with sparse arrays.
    '''
    n = len(args)
    if   n == 0: return np.asarray(0)
    elif n == 1: return np.asarray(args[0])
    elif n >  2: return reduce(plus, args)
    (a,b) = args
    if sps.issparse(a):
        if not sps.issparse(b):
            b = np.asarray(b)
            if len(b.shape) == 0: b = np.reshape(b, (1,1))
    elif sps.issparse(b):
        a = np.asarray(a)
        if len(a.shape) == 0: a = np.reshape(a, (1,1))
    else:
        a = np.asarray(a)
        b = np.asarray(b)
    return a + b 
Example #7
Source Project: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def ctimes(*args):
    '''
    ctimes(a, b...) returns the product of all the values as a numpy array object. Like numpy's
      multiply function or a*b syntax, times will thread over the latest dimension possible; thus
      if a.shape is (4,2) and b.shape is 2, times(a,b) is a equivalent to a * b.

    Unlike numpy's multiply function, ctimes works with sparse matrices and will reify them.
    '''
    n = len(args)
    if   n == 0: return np.asarray(0)
    elif n == 1: return np.asarray(args[0])
    elif n >  2: return reduce(plus, args)
    (a,b) = args
    if   sps.issparse(a): return a.multiply(b)
    elif sps.issparse(b): return b.multiply(a)
    else:                 return np.asarray(a) * b 
Example #8
Source Project: cs294-112_hws   Author: xuwd11   File: tabulate.py    License: MIT License 6 votes vote down vote up
def _column_type(strings, has_invisible=True):
    """The least generic type all column values are convertible to.

    >>> _column_type(["1", "2"]) is _int_type
    True
    >>> _column_type(["1", "2.3"]) is _float_type
    True
    >>> _column_type(["1", "2.3", "four"]) is _text_type
    True
    >>> _column_type(["four", '\u043f\u044f\u0442\u044c']) is _text_type
    True
    >>> _column_type([None, "brux"]) is _text_type
    True
    >>> _column_type([1, 2, None]) is _int_type
    True
    >>> import datetime as dt
    >>> _column_type([dt.datetime(1991,2,19), dt.time(17,35)]) is _text_type
    True

    """
    types = [_type(s, has_invisible) for s in strings ]
    return reduce(_more_generic, types, int) 
Example #9
Source Project: fine-lm   Author: akzaidi   File: rl.py    License: MIT License 6 votes vote down vote up
def feed_forward_categorical_fun(action_space, config, observations):
  """Feed-forward categorical."""
  if not isinstance(action_space, gym.spaces.Discrete):
    raise ValueError("Expecting discrete action space.")
  flat_observations = tf.reshape(observations, [
      tf.shape(observations)[0], tf.shape(observations)[1],
      functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
  with tf.variable_scope("network_parameters"):
    with tf.variable_scope("policy"):
      x = flat_observations
      for size in config.policy_layers:
        x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
      logits = tf.contrib.layers.fully_connected(x, action_space.n,
                                                 activation_fn=None)
    with tf.variable_scope("value"):
      x = flat_observations
      for size in config.value_layers:
        x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
      value = tf.contrib.layers.fully_connected(x, 1, None)[..., 0]
  policy = tf.contrib.distributions.Categorical(logits=logits)
  return NetworkOutput(policy, value, lambda a: a) 
Example #10
Source Project: fine-lm   Author: akzaidi   File: rl.py    License: MIT License 6 votes vote down vote up
def dense_bitwise_categorical_fun(action_space, config, observations):
  """Dense network with bitwise input and categorical output."""
  del config
  obs_shape = common_layers.shape_list(observations)
  x = tf.reshape(observations, [-1] + obs_shape[2:])

  with tf.variable_scope("network_parameters"):
    with tf.variable_scope("dense_bitwise"):
      x = discretization.int_to_bit_embed(x, 8, 32)
      flat_x = tf.reshape(
          x, [obs_shape[0], obs_shape[1],
              functools.reduce(operator.mul, x.shape.as_list()[1:], 1)])

      x = tf.contrib.layers.fully_connected(flat_x, 256, tf.nn.relu)
      x = tf.contrib.layers.fully_connected(flat_x, 128, tf.nn.relu)

      logits = tf.contrib.layers.fully_connected(x, action_space.n,
                                                 activation_fn=None)

      value = tf.contrib.layers.fully_connected(
          x, 1, activation_fn=None)[..., 0]
      policy = tf.contrib.distributions.Categorical(logits=logits)

  return NetworkOutput(policy, value, lambda a: a) 
Example #11
Source Project: fine-lm   Author: akzaidi   File: common_attention.py    License: MIT License 6 votes vote down vote up
def gather_indices_2d(x, block_shape, block_stride):
  """Getting gather indices."""
  # making an identity matrix kernel
  kernel = tf.eye(block_shape[0] * block_shape[1])
  kernel = reshape_range(kernel, 0, 1, [block_shape[0], block_shape[1], 1])
  # making indices [1, h, w, 1] to appy convs
  x_shape = common_layers.shape_list(x)
  indices = tf.range(x_shape[2] * x_shape[3])
  indices = tf.reshape(indices, [1, x_shape[2], x_shape[3], 1])
  indices = tf.nn.conv2d(
      tf.cast(indices, tf.float32),
      kernel,
      strides=[1, block_stride[0], block_stride[1], 1],
      padding="VALID")
  # making indices [num_blocks, dim] to gather
  dims = common_layers.shape_list(indices)[:3]
  if all([isinstance(dim, int) for dim in dims]):
    num_blocks = functools.reduce(operator.mul, dims, 1)
  else:
    num_blocks = tf.reduce_prod(dims)
  indices = tf.reshape(indices, [num_blocks, -1])
  return tf.cast(indices, tf.int32) 
Example #12
Source Project: lirpg   Author: Hwhitetooth   File: ddpg.py    License: MIT License 6 votes vote down vote up
def setup_critic_optimizer(self):
        logger.info('setting up critic optimizer')
        normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1])
        self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
        if self.critic_l2_reg > 0.:
            critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
            for var in critic_reg_vars:
                logger.info('  regularizing: {}'.format(var.name))
            logger.info('  applying l2 regularization with {}'.format(self.critic_l2_reg))
            critic_reg = tc.layers.apply_regularization(
                tc.layers.l2_regularizer(self.critic_l2_reg),
                weights_list=critic_reg_vars
            )
            self.critic_loss += critic_reg
        critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
        critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
        logger.info('  critic shapes: {}'.format(critic_shapes))
        logger.info('  critic params: {}'.format(critic_nb_params))
        self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
        self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
            beta1=0.9, beta2=0.999, epsilon=1e-08) 
Example #13
Source Project: mindustry-modding   Author: SimonWoodburyForget   File: to_wiki.py    License: GNU General Public License v3.0 6 votes vote down vote up
def normalize(md):
    '''Normalize anchors.'''
    def on_match(link):
        desc = link.group(1)
        old = link.group(2)
        href = (link.group(2)
                .lower()
                .replace('%20', '-')
                .replace(" ", "-")
                .replace("~", "")
                .replace(".", ""))
        old, new = f'[{desc}]({old})', f'[{desc}]({href})'
        print(old, new)
        return old, new

    replacers = set((on_match(x) for x in re.finditer(r'\[([^\]\[]*)\]\((#[^\)]*)\)', md)))
    return ft.reduce(lambda md, x: md.replace(x[0], x[1]), replacers, md) 
Example #14
Source Project: HardRLWithYoutube   Author: MaxSobolMark   File: ddpg.py    License: MIT License 6 votes vote down vote up
def setup_critic_optimizer(self):
        logger.info('setting up critic optimizer')
        normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1])
        self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
        if self.critic_l2_reg > 0.:
            critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
            for var in critic_reg_vars:
                logger.info('  regularizing: {}'.format(var.name))
            logger.info('  applying l2 regularization with {}'.format(self.critic_l2_reg))
            critic_reg = tc.layers.apply_regularization(
                tc.layers.l2_regularizer(self.critic_l2_reg),
                weights_list=critic_reg_vars
            )
            self.critic_loss += critic_reg
        critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
        critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
        logger.info('  critic shapes: {}'.format(critic_shapes))
        logger.info('  critic params: {}'.format(critic_nb_params))
        self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
        self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
            beta1=0.9, beta2=0.999, epsilon=1e-08) 
Example #15
Source Project: firmanal   Author: kyechou   File: afl.py    License: MIT License 6 votes vote down vote up
def extract(iid, bindir):
    print('Extracting binaries......')
    query = '''select filename from object_to_image where iid=''' + iid + ''' and score>0 and (mime='application/x-executable; charset=binary' or mime='application/x-object; charset=binary' or mime='application/x-sharedlib; charset=binary') order by score DESC;'''
    wanted = dbquery(query)
    wanted = reduce((lambda a, b: a + b), wanted)
    wanted = map((lambda a: '.' + a), wanted)
    wanted = reduce((lambda a, b: a + ' ' + b), wanted)
    cmd = 'tar xf ' + bindir + '/../../../../images/' + iid + '.tar.gz -C ' + bindir + ' ' + wanted
    subprocess.run([cmd], shell=True)

    print('Extracting library links......')
    query = '''select filename from object_to_image where iid=''' + iid + ''' and regular_file='f';'''
    wanted = dbquery(query)
    wanted = reduce((lambda a, b: a + b), wanted)
    wanted = filter((lambda a: 'lib' in a), wanted)
    wanted = map((lambda a: '.' + a), wanted)
    wanted = reduce((lambda a, b: a + ' ' + b), wanted)
    cmd = 'tar xf ' + bindir + '/../../../../images/' + iid + '.tar.gz -C ' + bindir + ' ' + wanted
    subprocess.run([cmd], shell=True) 
Example #16
Source Project: pyscf   Author: pyscf   File: x2c.py    License: Apache License 2.0 6 votes vote down vote up
def _get_r(s, snesc):
    # R^dag \tilde{S} R = S
    # R = S^{-1/2} [S^{-1/2}\tilde{S}S^{-1/2}]^{-1/2} S^{1/2}
    w, v = numpy.linalg.eigh(s)
    idx = w > 1e-14
    v = v[:,idx]
    w_sqrt = numpy.sqrt(w[idx])
    w_invsqrt = 1 / w_sqrt

    # eigenvectors of S as the new basis
    snesc = reduce(numpy.dot, (v.conj().T, snesc, v))
    r_mid = numpy.einsum('i,ij,j->ij', w_invsqrt, snesc, w_invsqrt)
    w1, v1 = numpy.linalg.eigh(r_mid)
    idx1 = w1 > 1e-14
    v1 = v1[:,idx1]
    r_mid = numpy.dot(v1/numpy.sqrt(w1[idx1]), v1.conj().T)
    r = numpy.einsum('i,ij,j->ij', w_invsqrt, r_mid, w_sqrt)
    # Back transform to AO basis
    r = reduce(numpy.dot, (v, r, v.conj().T))
    return r 
Example #17
Source Project: pyscf   Author: pyscf   File: test_x2c_hess.py    License: Apache License 2.0 6 votes vote down vote up
def _sqrt2(a0, a1i, a1j, a2ij):
    '''Solving second order derivative of x^2 = a'''
    w, v = scipy.linalg.eigh(a0)
    w = numpy.sqrt(w)
    a1i = reduce(numpy.dot, (v.conj().T, a1i, v))
    x1i = a1i / (w[:,None] + w)

    a1j = reduce(numpy.dot, (v.conj().T, a1j, v))
    x1j = a1j / (w[:,None] + w)

    a2ij = reduce(numpy.dot, (v.conj().T, a2ij, v))
    tmp = x1i.dot(x1j)
    a2ij -= tmp + tmp.conj().T
    x2 = a2ij / (w[:,None] + w)
    x2 = reduce(numpy.dot, (v, x2, v.conj().T))
    return x2 
Example #18
Source Project: pyscf   Author: pyscf   File: cdft.py    License: Apache License 2.0 6 votes vote down vote up
def fast_iao_mullikan_pop(mf,cell,a=None):
    '''
    Input: mf -- a preconverged mean fild object
    Returns: mullikan populaion analysis in the basisIAO a
    '''

    #
    # here we convert the density matrix to the IAO basis
    #
    if a is None:
        a = numpy.eye(mf.make_rdm1().shape[1])
    #converts the occupied MOs to the IAO basis
    ovlpS = mf.get_ovlp()
    CIb = reduce(numpy.dot, (a.T, ovlpS , mf.make_rdm1()))

    #
    # This is the mullikan population below here
    #

    mo_occ = mf.mo_coeff[:,mf.mo_occ>0]
    mo_occ = reduce(numpy.dot, (a.T, mf.get_ovlp(), mo_occ))
    dm = numpy.dot(mo_occ, mo_occ.T) * 2
    pmol = cell.copy()
    pmol.build(False, False, basis='minao')
    return mf.mulliken_pop(pmol, dm, s=numpy.eye(pmol.nao_nr())) 
Example #19
Source Project: pyscf   Author: pyscf   File: kuhf.py    License: Apache License 2.0 6 votes vote down vote up
def spin_square(self, mo_coeff=None, s=None):
        '''Treating the k-point sampling wfn as a giant Slater determinant,
        the spin_square value is the <S^2> of the giant determinant.
        '''
        nkpts = len(self.kpts)
        if mo_coeff is None:
            mo_a = [self.mo_coeff[0][k][:,self.mo_occ[0][k]>0] for k in range(nkpts)]
            mo_b = [self.mo_coeff[1][k][:,self.mo_occ[1][k]>0] for k in range(nkpts)]
        else:
            mo_a, mo_b = mo_coeff
        if s is None:
            s = self.get_ovlp()

        nelec_a = sum([mo_a[k].shape[1] for k in range(nkpts)])
        nelec_b = sum([mo_b[k].shape[1] for k in range(nkpts)])
        ssxy = (nelec_a + nelec_b) * .5
        for k in range(nkpts):
            sij = reduce(np.dot, (mo_a[k].T.conj(), s[k], mo_b[k]))
            ssxy -= np.einsum('ij,ij->', sij.conj(), sij).real
        ssz = (nelec_b-nelec_a)**2 * .25
        ss = ssxy + ssz
        s = np.sqrt(ss+.25) - .5
        return ss, s*2+1 
Example #20
Source Project: pyscf   Author: pyscf   File: khf.py    License: Apache License 2.0 6 votes vote down vote up
def canonicalize(mf, mo_coeff_kpts, mo_occ_kpts, fock=None):
    if fock is None:
        dm = mf.make_rdm1(mo_coeff_kpts, mo_occ_kpts)
        fock = mf.get_fock(dm=dm)
    mo_coeff = []
    mo_energy = []
    for k, mo in enumerate(mo_coeff_kpts):
        mo1 = np.empty_like(mo)
        mo_e = np.empty_like(mo_occ_kpts[k])
        occidx = mo_occ_kpts[k] == 2
        viridx = ~occidx
        for idx in (occidx, viridx):
            if np.count_nonzero(idx) > 0:
                orb = mo[:,idx]
                f1 = reduce(np.dot, (orb.T.conj(), fock[k], orb))
                e, c = scipy.linalg.eigh(f1)
                mo1[:,idx] = np.dot(orb, c)
                mo_e[idx] = e
        mo_coeff.append(mo1)
        mo_energy.append(mo_e)
    return mo_energy, mo_coeff 
Example #21
Source Project: vergeml   Author: mme   File: loader.py    License: MIT License 5 votes vote down vote up
def _calculate_num_samples(self, split):
        """Calculate the total number of samples after applying ops.
        """
        num_samples = self.input.num_samples(split)

        # calculate how much the samples will be augmented after going through ops
        multiplier = reduce(operator.mul, map(lambda op: _get_multiplier(split, op), self.ops), 1)

        return int(num_samples * multiplier) 
Example #22
Source Project: neural-fingerprinting   Author: StephanZheng   File: utils_mnist.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def download_and_parse_mnist_file(file_name, datadir=None, force=False):
    file_name = maybe_download_mnist_file(file_name, datadir=datadir,
                                          force=force)

    # Open the file and unzip it if necessary
    if os.path.splitext(file_name)[1] == '.gz':
        open_fn = gzip.open
    else:
        open_fn = open

    # Parse the file
    with open_fn(file_name, 'rb') as file_descriptor:
        header = file_descriptor.read(4)
        assert len(header) == 4

        zeros, data_type, n_dims = struct.unpack('>HBB', header)
        assert zeros == 0

        hex_to_data_type = {
            0x08: 'B',
            0x09: 'b',
            0x0b: 'h',
            0x0c: 'i',
            0x0d: 'f',
            0x0e: 'd'}
        data_type = hex_to_data_type[data_type]

        dim_sizes = struct.unpack(
            '>' + 'I' * n_dims,
            file_descriptor.read(4 * n_dims))

        data = array.array(data_type, file_descriptor.read())
        data.byteswap()

        desired_items = functools.reduce(operator.mul, dim_sizes)
        assert len(data) == desired_items
        return np.array(data).reshape(dim_sizes) 
Example #23
Source Project: multibootusb   Author: mbusb   File: test-grub.py    License: GNU General Public License v2.0 5 votes vote down vote up
def os_path_exists(f):
    chunks = reduce(lambda accum, x : accum + x.split('/'), f.split('\\'), [])
    if chunks[-1] in ['multibootusb.log', 'loopback.cfg']:
        return True
    if 'arch' in chunks:
        return True
    if chunks[1] in ['rootfs1.gz', 'rootfs2.gz']:
        return True
    if chunks == ['multibootusb', 'debian-sid', 'boot', 'rootfs4.gz']:
        return True
    return False 
Example #24
Source Project: models   Author: kipoi   File: pretrained_model_reloaded_th.py    License: MIT License 5 votes vote down vote up
def forward(self, input):
        return reduce(self.lambda_func,self.forward_prepare(input)) 
Example #25
Source Project: models   Author: kipoi   File: convert_Basset_to_pytorch.py    License: MIT License 5 votes vote down vote up
def forward(self, input):
        # result is a Variable
        return reduce(self.lambda_func,self.forward_prepare(input)) 
Example #26
Source Project: models   Author: kipoi   File: model_architecture.py    License: MIT License 5 votes vote down vote up
def forward(self, input):
        return reduce(self.lambda_func,self.forward_prepare(input)) 
Example #27
Source Project: python-clean-architecture   Author: pcah   File: abstract.py    License: MIT License 5 votes vote down vote up
def _reduced_filter(self) -> t.Optional[Predicate]:
        """Before evaluation, sum up all filter predicates into a single one"""
        return None if self._is_trivial else reduce(and_, self._filters)

    # lazy queries 
Example #28
Source Project: VSE-C   Author: ExplorerFreda   File: numeral.py    License: MIT License 5 votes vote down vote up
def valid(word):
    # check number
    try:
        numbers = [w2n.word_to_num(it) for it in word.split()]
        if len(numbers) > 1:
            return False
    except ValueError:
        return False
    # check English
    chars = map(lambda c: 'a' <= c <= 'z', word)
    return reduce(lambda a, b: a or b, chars, False) 
Example #29
Source Project: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 5 votes vote down vote up
def compose(*args):
    '''
    compose(g, h...) yields a potential function f that is the result of composing together all the
      arguments g, h, etc. after calling to_potential() on each. The result is defined such that
      f(x) is equivalent to g(h(...(x))).
    '''
    return reduce(lambda h,g: PotentialComposition(g,h), reversed(list(map(to_potential, args)))) 
Example #30
Source Project: neuropythy   Author: noahbenson   File: retinotopy.py    License: GNU Affero General Public License v3.0 5 votes vote down vote up
def fit_pRF_radius(ctx, retinotopy=Ellipsis, mask=None, weight=Ellipsis, slope_only=False):
    '''
    fit_pRF_radius(ctx) fits a line, m*eccen + b, to the pRF radius and yields the tuple (m, b).

    The following options may be given:
      * retinotopy (default: Ellipsis) specifies the prefix for the retinotopy (passed to
        retinotopy_data() to find the retinotopic dataset).
      * mask (default: None) specifies the mask over which to perform the calculation. This is
        passed to the to_mask() function. In the case that mask is a set or frozenset, then it is
        treated as a conjunction (intersection) of masks.
      * weight (default: None) specifies that a weight should be used; if this is True or Ellipsis,
        will use the variance_explained if it is part of the retinotopy dataset; if this is False or
        None, uses no weight; otherwise, this must be a weight property or property name.
      * slope_only (default: False) may be set to True to instead fit radius = m*eccen and return
        only m.
    '''
    rdat = retinotopy_data(ctx, retinotopy)
    if 'radius' not in rdat: raise ValueError('No pRF radius found in dataset %s' % retinotopy)
    rad = rdat['radius']
    (ang,ecc) = as_retinotopy(rdat, 'visual')
    if isinstance(mask, (set, frozenset)):
        mask = reduce(np.intersect1d, [ctx.mask(m, indices=True) for m in mask])
    else: mask = ctx.mask(mask, indices=True)
    # get a weight if provided:
    if weight in [False, None]: wgt = np.ones(rad.shape)
    elif weight in [True, Ellipsis]:
        if 'variance_explained' in rdat: wgt = rdat['variance_explained']
        else: wgt = np.ones(rad.shape)
    else: wgt = ctx.property(weight)
    # get the relevant eccen and radius values
    (ecc,rad,wgt) = [x[mask] for x in (ecc,rad,wgt)]
    # fit a line...
    if slope_only:
        ecc = np.reshape(ecc * wgt, (len(ecc), 1))
        rad = np.reshape(rad * wgt, (len(rad), 1))
        return np.linalg.lstsq(ecc, rad)[0]
    else:
        return tuple(np.polyfit(ecc, rad, 1, w=wgt))