コード例 #1
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ファイル: norm.py プロジェクト: giserh/cvxpy
def norm(x, p=2, axis=None):
    """Wrapper on the different norm atoms.

    Parameters
    ----------
    x : Expression or numeric constant
        The value to take the norm of.
    p : int or str, optional
        The type of norm.

    Returns
    -------
    Expression
        An Expression representing the norm.
    """
    x = Expression.cast_to_const(x)
    if p == 1:
        return pnorm(x, 1, axis)
    elif p == "inf":
        return pnorm(x, 'inf', axis)
    elif p == "nuc":
        return normNuc(x)
    elif p == "fro":
        return pnorm(x, 2, axis)
    elif p == 2:
        if x.is_matrix():
            return sigma_max(x)
        else:
            return pnorm(x, 2, axis)
    else:
        return pnorm(x, p, axis)
コード例 #2
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ファイル: norm.py プロジェクト: Cloud2016/cvxpy
def norm(x, p=2, axis=None):
    """Wrapper on the different norm atoms.

    Parameters
    ----------
    x : Expression or numeric constant
        The value to take the norm of.
    p : int or str, optional
        The type of norm.

    Returns
    -------
    Expression
        An Expression representing the norm.
    """
    x = Expression.cast_to_const(x)
    # Norms for scalars same as absolute value.
    if p == 1 or x.is_scalar():
        return pnorm(x, 1, axis)
    elif p == "inf":
        return pnorm(x, 'inf', axis)
    elif p == "nuc":
        return normNuc(x)
    elif p == "fro":
        return pnorm(x, 2, axis)
    elif p == 2:
        if axis is None and x.is_matrix():
            return sigma_max(x)
        else:
            return pnorm(x, 2, axis)
    else:
        return pnorm(x, p, axis)
コード例 #3
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ファイル: norm.py プロジェクト: Adarsh-Barik/cvxpy
def norm(x, p=2):
    """Wrapper on the different norm atoms.

    Parameters
    ----------
    x : Expression or numeric constant
        The value to take the norm of.
    p : int or str, optional
        The type of norm.

    Returns
    -------
    Expression
        An Expression representing the norm.
    """
    x = Expression.cast_to_const(x)
    if p == 1:
        return pnorm(x, 1)
    elif p == "inf":
        return pnorm(x, 'inf')
    elif p == "nuc":
        return normNuc(x)
    elif p == "fro":
        return pnorm(x, 2)
    elif p == 2:
        if x.is_matrix():
            return sigma_max(x)
        else:
            return pnorm(x, 2)
    else:
        return pnorm(x, p)
コード例 #4
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ファイル: problem.py プロジェクト: giserh/cvxpy
 def DCCP_ini(self):
     dom_constr = self.objective.args[0].domain
     for arg in self.constraints:
         for dom in arg.args[0].domain:
             dom_constr.append(dom)
         for dom in arg.args[1].domain:
             dom_constr.append(dom)
     var_store = []
     init_flag = []
     for var in self.variables():
         var_store.append(np.zeros((var._rows,var._cols)))
         init_flag.append(var.value is None)
     times = 3
     for t in range(times):
         ini_cost = 0
         var_ind = 0
         for var in self.variables():
             if init_flag[var_ind]:
                 var.value = np.random.randn(var._rows,var._cols)*10
             ini_cost += pnorm(var-var.value,2)
             var_ind += 1
         ini_obj = Minimize(ini_cost)
         ini_prob = Problem(ini_obj,dom_constr)
         ini_prob.solve()
         var_ind = 0
         for var in self.variables():
             var_store[var_ind] = var_store[var_ind] + var.value/float(times)
             var_ind += 1
     var_ind = 0
     for var in self.variables():
         var.value = var_store[var_ind]
         var_ind += 1
コード例 #5
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def norm(x, p=2, axis=None):
    """Wrapper on the different norm atoms.

    Parameters
    ----------
    x : Expression or numeric constant
        The value to take the norm of.  If `x` is 2D and `axis` is None,
        this function constructs a matrix norm.
    p : int or str, optional
        The type of norm. Valid options include any positive integer,
        'fro' (for frobenius), 'nuc' (sum of singular values), np.inf or
        'inf' (infinity norm).
    axis : The axis along which to apply the norm, if any.

    Returns
    -------
    Expression
        An Expression representing the norm.
    """
    x = Expression.cast_to_const(x)
    # matrix norms take precedence
    num_nontrivial_idxs = sum([d > 1 for d in x.shape])
    if axis is None and x.ndim == 2:
        if p == 1:  # matrix 1-norm
            return cvxpy.atoms.max(norm1(x, axis=0))
        # Frobenius norm
        elif p == 'fro' or (p == 2 and num_nontrivial_idxs == 1):
            return pnorm(vec(x), 2)
        elif p == 2:  # matrix 2-norm is largest singular value
            return sigma_max(x)
        elif p == 'nuc':  # the nuclear norm (sum of singular values)
            return normNuc(x)
        elif p in [np.inf, "inf", "Inf"]:  # the matrix infinity-norm
            return cvxpy.atoms.max(norm1(x, axis=1))
        else:
            raise RuntimeError('Unsupported matrix norm.')
    else:
        if p == 1 or x.is_scalar():
            return norm1(x, axis=axis)
        elif p in [np.inf, "inf", "Inf"]:
            return norm_inf(x, axis)
        else:
            return pnorm(x, p, axis)
コード例 #6
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ファイル: norm.py プロジェクト: yuhaopp/cvxpy
def norm(x, p=2, axis=None):
    """Wrapper on the different norm atoms.

    Parameters
    ----------
    x : Expression or numeric constant
        The value to take the norm of.
    p : int or str, optional
        The type of norm.

    Returns
    -------
    Expression
        An Expression representing the norm.
    """
    x = Expression.cast_to_const(x)
    # matrix norms take precedence
    if axis is None and x.ndim == 2:
        if p == 1:  # matrix 1-norm
            return cvxpy.atoms.max(norm1(x, axis=0))
        elif p == 2:  # matrix 2-norm is largest singular value
            return sigma_max(x)
        elif p == 'nuc':  # the nuclear norm (sum of singular values)
            return normNuc(x)
        elif p == 'fro':  # Frobenius norm
            return pnorm(vec(x), 2)
        elif p in [np.inf, "inf", "Inf"]:  # the matrix infinity-norm
            return cvxpy.atoms.max(norm1(x, axis=1))
        else:
            raise RuntimeError('Unsupported matrix norm.')
    else:
        if p == 1 or x.is_scalar():
            return norm1(x, axis=axis)
        elif p in [np.inf, "inf", "Inf"]:
            return norm_inf(x, axis)
        else:
            return pnorm(x, p, axis)
コード例 #7
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ファイル: harmonic_mean.py プロジェクト: zhangys2/cvxpy
def harmonic_mean(x):
    """The harmonic mean of ``x``.

    Parameters
    ----------
    x : Expression or numeric
        The expression whose harmonic mean is to be computed. Must have
        positive entries.

    Returns
    -------
    Expression
        .. math::
            \\frac{n}{\\left(\\sum_{i=1}^{n} x_i^{-1} \\right)},

        where :math:`n` is the length of :math:`x`.
    """
    x = Expression.cast_to_const(x)
    # TODO(akshayka): Behavior of the below is incorrect when x has negative
    # entries. Either fail fast or provide a correct expression with
    # unknown curvature.
    return x.size*pnorm(x, -1)
コード例 #8
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def normInf(x, axis=None):
    return pnorm(x, 'inf', axis)
コード例 #9
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ファイル: harmonic_mean.py プロジェクト: jacklzhu/cvxpy
def harmonic_mean(x):
    return np.prod(x.size)*pnorm(x, -1)
コード例 #10
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ファイル: harmonic_mean.py プロジェクト: wolfws/cvxpy
def harmonic_mean(x):
    x = Expression.cast_to_const(x)
    return np.prod(x.size)*pnorm(x, -1)
コード例 #11
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ファイル: norm1.py プロジェクト: ChupingYang/cvxpy
def norm1(x, axis=None):
    return pnorm(x, 1, axis)
コード例 #12
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def norm2(x):
    return pnorm(x, 2)
コード例 #13
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ファイル: norm2.py プロジェクト: WanliXue/BF_implemation
def norm2(x, axis=None):
    return pnorm(x, 2, axis)
コード例 #14
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ファイル: norm1.py プロジェクト: wolfws/cvxpy
def norm1(x, axis=None):
    return pnorm(x, 1, axis)
コード例 #15
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def normInf(x):
    return pnorm(x, 'inf')
コード例 #16
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def norm1(x):
    return pnorm(x, 1)