Python numpy.vdot方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def guessInitial(self):
        nrm = np.linalg.norm(self.x0)
        self.x0 *= 1./nrm
        self.currentSize = self.nEigen
        for i in range(self.currentSize):
            self.vlist[i] *= 0.0
            self.vlist[i,:] += np.random.rand(self.size)
            self.vlist[i,i] /= np.linalg.norm(self.vlist[i,i])
            self.vlist[i,i] *= 12.
            for j in range(i):
                fac = np.vdot( self.vlist[j,:], self.vlist[i,:] )
                self.vlist[i,:] -= fac * self.vlist[j,:]
            self.vlist[i,:] /= np.linalg.norm(self.vlist[i,:])
        for i in range(self.currentSize):
            self.cv = self.vlist[i].copy()
            self.hMult()
            self.Avlist[i] = self.cAv.copy()
        self.constructSubspace() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def gramSchmidtCurrentVec(self,northo):
        for k in range(northo):
            fac = np.vdot( self.vlist[k,:], self.cv )
            self.cv -= fac * self.vlist[k,:] #/ np.vdot(self.vlist[i],self.vlist[i])
        cvnorm = np.linalg.norm(self.cv)
        if cvnorm < 1e-4:
            self.cv = np.random.rand(self.size)
            for k in range(northo):
                fac = np.vdot( self.vlist[k,:], self.cv )
                self.cv -= fac * self.vlist[k,:] #/ np.vdot(self.vlist[i],self.vlist[i])
            ########################################################################
            #  Sometimes some of the created vectors are linearly dependent.  i
            #  To get around this I'm not sure what to do other than to throw that
            #  solution out and start at that eigenvalue
            ########################################################################
            print(" ERROR!!!! ... restarting at eigenvalue #%" % \
                        (northo, cvnorm))
            self.ciEig = northo
        self.cv /= np.linalg.norm(self.cv) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def checkDeflate(self):
        if self.currentSize == self.maxM-1:
            self.deflated = 1
            for i in range(self.nEigen):
                self.sol[:self.currentSize] = self.evecs[:self.currentSize,i]
                self.constructSolV()            # Finds the "best" eigenvector for this eigenvalue
                self.Avlist[i,:] = self.cv.copy() # Puts this guess in self.Avlist rather than self.vlist for now...
                                                # since this would mess up self.constructSolV()'s solution
            for i in range(self.nEigen):
                self.cv = self.Avlist[i,:].copy() # This is actually the "best" eigenvector v, not A*v (see above)
                self.gramSchmidtCurrentVec(i)
                self.vlist[i,:] = self.cv.copy()

            orthog = 0.0
            for j in range(self.nEigen):
                for i in range(j):
                    orthog += np.vdot(self.vlist[j,:],self.vlist[i,:])**2.0

            for i in range(self.nEigen):
                self.cv = self.vlist[i].copy() # This is actually the "best" eigenvector v, not A*v (see above)
                self.hMult()                   # Use current vector cv to create cAv
                self.Avlist[i] = self.cAv.copy() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def testVdotExecution(self):
        a_data = np.array([1 + 2j, 3 + 4j])
        b_data = np.array([5 + 6j, 7 + 8j])
        a = tensor(a_data, chunk_size=1)
        b = tensor(b_data, chunk_size=1)

        t = vdot(a, b)

        res = self.executor.execute_tensor(t)[0]
        expected = np.vdot(a_data, b_data)
        np.testing.assert_equal(res, expected)

        a_data = np.array([[1, 4], [5, 6]])
        b_data = np.array([[4, 1], [2, 2]])
        a = tensor(a_data, chunk_size=1)
        b = tensor(b_data, chunk_size=1)

        t = vdot(a, b)

        res = self.executor.execute_tensor(t)[0]
        expected = np.vdot(a_data, b_data)
        np.testing.assert_equal(res, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def solve(self, f, tol=0):
        dx = -self.alpha*f

        n = len(self.dx)
        if n == 0:
            return dx

        df_f = np.empty(n, dtype=f.dtype)
        for k in xrange(n):
            df_f[k] = vdot(self.df[k], f)

        try:
            gamma = solve(self.a, df_f)
        except LinAlgError:
            # singular; reset the Jacobian approximation
            del self.dx[:]
            del self.df[:]
            return dx

        for m in xrange(n):
            dx += gamma[m]*(self.dx[m] + self.alpha*self.df[m])
        return dx 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def matvec(self, f):
        dx = -f/self.alpha

        n = len(self.dx)
        if n == 0:
            return dx

        df_f = np.empty(n, dtype=f.dtype)
        for k in xrange(n):
            df_f[k] = vdot(self.df[k], f)

        b = np.empty((n, n), dtype=f.dtype)
        for i in xrange(n):
            for j in xrange(n):
                b[i,j] = vdot(self.df[i], self.dx[j])
                if i == j and self.w0 != 0:
                    b[i,j] -= vdot(self.df[i], self.df[i])*self.w0**2*self.alpha
        gamma = solve(b, df_f)

        for m in xrange(n):
            dx += gamma[m]*(self.df[m] + self.dx[m]/self.alpha)
        return dx 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def trace(self, **kwargs):
        r"""Trace of the density operator corresponding to the state.

        For pure states the trace corresponds to the squared norm of the ket vector.

        For physical states this should always be 1, any deviations from this value are due
        to numerical errors and Hilbert space truncation artefacts.

        Returns:
          float: trace of the state
        """
        # pylint: disable=unused-argument
        if self.is_pure:
            return np.vdot(self.ket(), self.ket()).real  # <s|s>

        # need some extra steps to trace over multimode matrices
        eqn_indices = [
            [indices[idx]] * 2 for idx in range(self._modes)
        ]  # doubled indices [['i','i'],['j','j'], ... ]
        eqn = "".join(
            chain.from_iterable(eqn_indices)
        )  # flatten indices into a single string 'iijj...'
        return np.einsum(eqn, self.dm()).real 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def symm_inner_product_array(
    num_states, num_vecs, max_vecs_per_node, verbosity=1):
    """
    Computes symmetric inner product array from known vecs (as in POD).
    """
    vec_handles = [mr.VecHandlePickle(join(data_dir, row_vec_name%row_num))
        for row_num in mr.range(num_vecs)]

    generate_vecs(data_dir, num_states, vec_handles)

    my_VS = mr.VectorSpaceHandles(
        inner_product=np.vdot, max_vecs_per_node=max_vecs_per_node,
        verbosity=verbosity)

    prof = cProfile.Profile()
    start_time = time.time()
    prof.runcall(my_VS.compute_symm_inner_product_array, vec_handles)
    total_time = time.time() - start_time
    prof.dump_stats('IP_symm_array_r%d.prof'%mr.parallel.get_rank())

    return total_time 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def setUp(self):
        if not os.access('.', os.W_OK):
            raise RuntimeError('Cannot write to current directory')
        self.test_dir = 'files_vectorspace_DELETE_ME'
        if not os.path.isdir(self.test_dir):
            parallel.call_from_rank_zero(os.mkdir, self.test_dir)

        self.max_vecs_per_proc = 10
        self.total_num_vecs_in_mem = (
            parallel.get_num_procs() * self.max_vecs_per_proc)

        self.vec_space = vspc.VectorSpaceHandles(
            inner_product=np.vdot, verbosity=0)
        self.vec_space.max_vecs_per_proc = self.max_vecs_per_proc

        # Default data members; set verbosity to 0 even though default is 1
        # so messages won't print during tests
        self.default_data_members = {
            'inner_product': np.vdot, 'max_vecs_per_node': 10000,
            'max_vecs_per_proc': (
                10000 * parallel.get_num_nodes() // parallel.get_num_procs()),
            'verbosity': 0, 'print_interval': 10, 'prev_print_time': 0.}
        parallel.barrier() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def test_sandwich(nr_sites, local_dim, rank, rgen, dtype):
    mps = factory.random_mpa(nr_sites, local_dim, rank,
                             randstate=rgen, dtype=dtype, normalized=True)
    mps2 = factory.random_mpa(nr_sites, local_dim, rank,
                              randstate=rgen, dtype=dtype, normalized=True)
    mpo = factory.random_mpa(nr_sites, [local_dim] * 2, rank,
                             randstate=rgen, dtype=dtype)
    mpo.canonicalize()
    mpo /= mp.trace(mpo)

    vec = mps.to_array().ravel()
    op = mpo.to_array_global().reshape([local_dim**nr_sites] * 2)
    res_arr = np.vdot(vec, np.dot(op, vec))
    res_mpo = mp.inner(mps, mp.dot(mpo, mps))
    res_sandwich = mp.sandwich(mpo, mps)
    assert_almost_equal(res_mpo, res_arr)
    assert_almost_equal(res_sandwich, res_arr)

    vec2 = mps2.to_array().ravel()
    res_arr = np.vdot(vec2, np.dot(op, vec))
    res_mpo = mp.inner(mps2, mp.dot(mpo, mps))
    res_sandwich = mp.sandwich(mpo, mps, mps2)
    assert_almost_equal(res_mpo, res_arr)
    assert_almost_equal(res_sandwich, res_arr) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def vecangle(v1, v2):
    """angle between two vectors v1, v2 in radians"""
    v0 = pt(0, 0)
        
    if np.allclose(v1, v0) or np.allclose(v2, v0):
        return np.nan
    
    if np.allclose(v1, v2):
        return 0
    
    num = np.vdot(v1, v2)
    denom = (np.linalg.norm(v1) * np.linalg.norm(v2))
    
    if np.isclose(num, denom):
        return 0
    
    return math.acos(num / denom) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def evaluate_with_statevector(self, quantum_state):
        """

        Args:
            quantum_state (numpy.ndarray): quantum state

        Returns:
            float: the mean value
            float: the standard deviation
        Raises:
            AquaError: if Operator is empty
        """
        if self.is_empty():
            raise AquaError("Operator is empty, check the operator.")

        avg = np.vdot(quantum_state, self._matrix.dot(quantum_state))
        return avg, 0.0 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def evaluate_with_statevector(self, quantum_state):
        """
        Args:
            quantum_state (numpy.ndarray): a quantum state.

        Returns:
            float: the mean value
            float: the standard deviation
        Raises:
            AquaError: if Operator is empty
        """
        if self.is_empty():
            raise AquaError("Operator is empty, check the operator.")
        # convert to matrix first?
        # pylint: disable=import-outside-toplevel
        from .op_converter import to_matrix_operator
        mat_op = to_matrix_operator(self)
        avg = np.vdot(quantum_state, mat_op._matrix.dot(quantum_state))
        return avg, 0.0

    # pylint: disable=arguments-differ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def constructSubspace(self):
        if self.totalIter == 0 or self.deflated == 1: # construct the full block of v^*Av
            for i in range(self.currentSize):
                for j in range(self.currentSize):
                   val = np.vdot(self.vlist[i],self.Avlist[j])
                   self.subH[i,j] = val
        else:
            for j in range(self.currentSize):
                if j <= (self.currentSize-1):
                    val = np.vdot(self.vlist[j],self.Avlist[self.currentSize-1])
                    self.subH[j,self.currentSize-1] = val
                if j < (self.currentSize-1):
                    val = np.vdot(self.vlist[self.currentSize-1],self.Avlist[j])
                    self.subH[self.currentSize-1,j] = val 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vdot [as 別名]
def computeResidual(self):
        self.res = self.cAv - self.cvEig * self.cv
        self.dres = np.vdot(self.res,self.res)**0.5
        #
        # gram-schmidt for residual vector
        #
        for i in range(self.currentSize):
            self.dgks[i] = np.vdot( self.vlist[i], self.res )
            self.res -= self.dgks[i]*self.vlist[i]
        #
        # second gram-schmidt to make them really orthogonal
        #
        for i in range(self.currentSize):
            self.dgks[i] = np.vdot( self.vlist[i], self.res )
            self.res -= self.dgks[i]*self.vlist[i]
        self.resnorm = np.linalg.norm(self.res)
        self.res /= self.resnorm

        orthog = 0.0
        for i in range(self.currentSize):
            orthog += np.vdot(self.res,self.vlist[i])**2.0
        orthog = orthog ** 0.5
        if not self.deflated:
            if VERBOSE:
                print("%3d %20.14f %20.14f  %10.4g" % (self.ciEig, self.cvEig.real, self.resnorm.real, orthog.real))
        #else:
        #    print "%3d %20.14f %20.14f %20.14f (deflated)" % (self.ciEig, self.cvEig,
        #                                                      self.resnorm, orthog)

        self.iteration += 1