python类matrix()的实例源码

test_linalg.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 42 收藏 0 点赞 0 评论 0
def test_mode_all_but_economic(self):
        a = array([[1, 2], [3, 4]])
        b = array([[1, 2], [3, 4], [5, 6]])
        for dt in "fd":
            m1 = a.astype(dt)
            m2 = b.astype(dt)
            self.check_qr(m1)
            self.check_qr(m2)
            self.check_qr(m2.T)
            self.check_qr(matrix(m1))
        for dt in "fd":
            m1 = 1 + 1j * a.astype(dt)
            m2 = 1 + 1j * b.astype(dt)
            self.check_qr(m1)
            self.check_qr(m2)
            self.check_qr(m2.T)
            self.check_qr(matrix(m1))
test_defmatrix.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def test_basic(self):
        A = np.array([[1, 2], [3, 4]])
        mA = matrix(A)
        assert_(np.all(mA.A == A))

        B = bmat("A,A;A,A")
        C = bmat([[A, A], [A, A]])
        D = np.array([[1, 2, 1, 2],
                      [3, 4, 3, 4],
                      [1, 2, 1, 2],
                      [3, 4, 3, 4]])
        assert_(np.all(B.A == D))
        assert_(np.all(C.A == D))

        E = np.array([[5, 6], [7, 8]])
        AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
        assert_(np.all(bmat([A, E]) == AEresult))

        vec = np.arange(5)
        mvec = matrix(vec)
        assert_(mvec.shape == (1, 5))
test_defmatrix.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def test_sum(self):
        """Test whether matrix.sum(axis=1) preserves orientation.
        Fails in NumPy <= 0.9.6.2127.
        """
        M = matrix([[1, 2, 0, 0],
                   [3, 4, 0, 0],
                   [1, 2, 1, 2],
                   [3, 4, 3, 4]])
        sum0 = matrix([8, 12, 4, 6])
        sum1 = matrix([3, 7, 6, 14]).T
        sumall = 30
        assert_array_equal(sum0, M.sum(axis=0))
        assert_array_equal(sum1, M.sum(axis=1))
        assert_equal(sumall, M.sum())

        assert_array_equal(sum0, np.sum(M, axis=0))
        assert_array_equal(sum1, np.sum(M, axis=1))
        assert_equal(sumall, np.sum(M))
test_defmatrix.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 38 收藏 0 点赞 0 评论 0
def test_basic(self):
        import numpy.linalg as linalg

        A = np.array([[1., 2.],
                      [3., 4.]])
        mA = matrix(A)
        assert_(np.allclose(linalg.inv(A), mA.I))
        assert_(np.all(np.array(np.transpose(A) == mA.T)))
        assert_(np.all(np.array(np.transpose(A) == mA.H)))
        assert_(np.all(A == mA.A))

        B = A + 2j*A
        mB = matrix(B)
        assert_(np.allclose(linalg.inv(B), mB.I))
        assert_(np.all(np.array(np.transpose(B) == mB.T)))
        assert_(np.all(np.array(np.transpose(B).conj() == mB.H)))
quaternion.py 文件源码 项目:joysix 作者: niberger 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def skew(v):
    return np.matrix([[0,-v[2,0],v[1,0]], [v[2,0],0,-v[0,0]], [-v[1,0],v[0,0],0]])
vector3.py 文件源码 项目:joysix 作者: niberger 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def vector3(x, y, z):
    return np.matrix([[x],[y],[z]])
vector3.py 文件源码 项目:joysix 作者: niberger 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def vector6(a, b, c, x, y, z):
    return np.matrix([[a],[b],[c],[x],[y],[z]])
vector3.py 文件源码 项目:joysix 作者: niberger 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def col(v):
    col = [[x] for x in v]
    return np.matrix(col)
tdose_utilities.py 文件源码 项目:TDOSE 作者: kasperschmidt 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def build_2D_cov_matrix(sigmax,sigmay,angle,verbose=True):
    """
    Build a covariance matrix for a 2D multivariate Gaussian

    --- INPUT ---
    sigmax          Standard deviation of the x-compoent of the multivariate Gaussian
    sigmay          Standard deviation of the y-compoent of the multivariate Gaussian
    angle           Angle to rotate matrix by in degrees (clockwise) to populate covariance cross terms
    verbose         Toggle verbosity
    --- EXAMPLE OF USE ---
    import tdose_utilities as tu
    covmatrix = tu.build_2D_cov_matrix(3,1,35)

    """
    if verbose: print ' - Build 2D covariance matrix with varinaces (x,y)=('+str(sigmax)+','+str(sigmay)+\
                      ') and then rotated '+str(angle)+' degrees'
    cov_orig      = np.zeros([2,2])
    cov_orig[0,0] = sigmay**2.0
    cov_orig[1,1] = sigmax**2.0

    angle_rad     = (180.0-angle) * np.pi/180.0 # The (90-angle) makes sure the same convention as DS9 is used
    c, s          = np.cos(angle_rad), np.sin(angle_rad)
    rotmatrix     = np.matrix([[c, -s], [s, c]])

    cov_rot       = np.dot(np.dot(rotmatrix,cov_orig),np.transpose(rotmatrix))  # performing rot * cov * rot^T

    return cov_rot
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
tdose_utilities.py 文件源码 项目:TDOSE 作者: kasperschmidt 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def normalize_2D_cov_matrix(covmatrix,verbose=True):
    """
    Calculate the normalization foctor for a multivariate gaussian from it's covariance matrix
    However, not that gaussian returned by tu.gen_2Dgauss() is normalized for scale=1

    --- INPUT ---
    covmatrix       covariance matrix to normaliz
    verbose         Toggle verbosity

    """
    detcov  = np.linalg.det(covmatrix)
    normfac = 1.0 / (2.0 * np.pi * np.sqrt(detcov) )

    return normfac
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
tdose_utilities.py 文件源码 项目:TDOSE 作者: kasperschmidt 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def analytic_convolution_gaussian(mu1,covar1,mu2,covar2):
    """
    The analytic vconvolution of two Gaussians is simply the sum of the two mean vectors
    and the two convariance matrixes

    --- INPUT ---
    mu1         The mean of the first gaussian
    covar1      The covariance matrix of of the first gaussian
    mu2         The mean of the second gaussian
    covar2      The covariance matrix of of the second gaussian

    """
    muconv    = mu1+mu2
    covarconv = covar1+covar2
    return muconv, covarconv

# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
main.py 文件源码 项目:FaceSwap 作者: Aravind-Suresh 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def get_facial_landmarks_from_mask(img, pts):
    rect = cv2.boundingRect(pts)
    rect = dlib.rectangle(rect[0], rect[1], rect[0] + rect[2], rect[1] + rect[3])
    return np.matrix([list(pt) for pt in pts]), rect
main.py 文件源码 项目:FaceSwap 作者: Aravind-Suresh 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def get_facial_landmarks(img):
    # No need to upsample
    rects = face_detector(img, 0)

    if len(rects) == 0:
        print "No faces"
        return None

    rect = rects[0]
    shape = shape_predictor(img, rect)
    return np.matrix([[pt.x, pt.y] for pt in shape.parts()]), rect
main.py 文件源码 项目:FaceSwap 作者: Aravind-Suresh 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def get_tm_opp(pts1, pts2):
    # Transformation matrix - ( Translation + Scaling + Rotation )
    # using Procuster analysis
    pts1 = np.float64(pts1)
    pts2 = np.float64(pts2)

    m1 = np.mean(pts1, axis = 0)
    m2 = np.mean(pts2, axis = 0)

    # Removing translation
    pts1 -= m1
    pts2 -= m2

    std1 = np.std(pts1)
    std2 = np.std(pts2)
    std_r = std2/std1

    # Removing scaling
    pts1 /= std1
    pts2 /= std2

    U, S, V = np.linalg.svd(np.transpose(pts1) * pts2)

    # Finding the rotation matrix
    R = np.transpose(U * V)

    return np.vstack([np.hstack((std_r * R,
        np.transpose(m2) - std_r * R * np.transpose(m1))), np.matrix([0.0, 0.0, 1.0])])
utils.py 文件源码 项目:spyking-circus 作者: spyking-circus 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def as_float_array(X, copy=True, force_all_finite=True):
    """Converts an array-like to an array of floats
    The new dtype will be np.float32 or np.float64, depending on the original
    type. The function can create a copy or modify the argument depending
    on the argument copy.
    Parameters
    ----------
    X : {array-like, sparse matrix}
    copy : bool, optional
        If True, a copy of X will be created. If False, a copy may still be
        returned if X's dtype is not a floating point type.
    force_all_finite : boolean (default=True)
        Whether to raise an error on np.inf and np.nan in X.
    Returns
    -------
    XT : {array, sparse matrix}
        An array of type np.float
    """
    if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
                                    and not sp.issparse(X)):
        return check_array(X, ['csr', 'csc', 'coo'], dtype=np.float64,
                           copy=copy, force_all_finite=force_all_finite,
                           ensure_2d=False)
    elif sp.issparse(X) and X.dtype in [np.float32, np.float64]:
        return X.copy() if copy else X
    elif X.dtype in [np.float32, np.float64]:  # is numpy array
        return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X
    else:
        return X.astype(np.float32 if X.dtype == np.int32 else np.float64)
utils.py 文件源码 项目:spyking-circus 作者: spyking-circus 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def get_precision(self):
        """Compute data precision matrix with the generative model.
        Equals the inverse of the covariance but computed with
        the matrix inversion lemma for efficiency.
        Returns
        -------
        precision : array, shape=(n_features, n_features)
            Estimated precision of data.
        """
        n_features = self.components_.shape[1]

        # handle corner cases first
        if self.n_components_ == 0:
            return np.eye(n_features) / self.noise_variance_
        if self.n_components_ == n_features:
            return linalg.inv(self.get_covariance())

        # Get precision using matrix inversion lemma
        components_ = self.components_
        exp_var = self.explained_variance_
        exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.)
        precision = np.dot(components_, components_.T) / self.noise_variance_
        precision.flat[::len(precision) + 1] += 1. / exp_var_diff
        precision = np.dot(components_.T,
                           np.dot(linalg.inv(precision), components_))
        precision /= -(self.noise_variance_ ** 2)
        precision.flat[::len(precision) + 1] += 1. / self.noise_variance_
        return precision
paramsearchGA_DopDep_nonlinear.py 文件源码 项目:Homology_BG 作者: jyotikab 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def diffD1D2(Flags):
    # To check if the difference between D1 and D2 amplifies downstream
    # First decide which model to use
    delay = 1
    if Flags == "Allsym":
        (d1,d2,fsi,ti,ta,stn,gpi,ipctx,A,B,params) = calcRates(Flags,delay)
        # D = Direct pathway, ID = Indirect pathway, HD = Hyperdirect pathway
        # Reducing a full recurrent matrix leads to postive and negative contributions in ID and HD instead of pure just positive contributions
        D = params['gpid1']
        print "Direct",D

        de1 = 1. + (params['d1d1']*params['fsifsi']) - (params['stnti']*params['stnstn'])
        Ex1 = (params['stnti']*params['d1d1']*params['fsifsi']*params['gpistn'])/de1        
        Ex2 = (params['gpid1']*params['stnstn']*params['d1ta']*params['fsifsi'])/de1        
        IDpos = params['stnti']*params['tid2']*(params['d1ta']*params['gpid1']*params['tistn']+ Ex1 + Ex2)

        print "IDpos",IDpos

        Ex3 = (params['d1ta']+params['d1ta']*params['tata']+((params['stnta']*params['fsiti']*params['d1fsi'])/de1))
        IDneg = params['gpid1']+params['gpiti']*params['tid2']+params['gpid1']*params['stnstn']*params['tid2']*Ex3  
        print "IDneg",IDneg

        HDpos = (params['jstnctx']*params['gpid1']*params['stnstn']*params['fsiti']*params['d1fsi'])/de1
        print "HDpos",HDpos
        Ex4 = params['jstnctx']*params['d1d1']*params['fsifsi']*params['stnti']*params['gpistn']
        Ex5 = params['jstnctx']*params['gpid1']*params['stnstn']*params['d1ta']*params['fsifsi']
        HDneg = (Ex4 + Ex5)/de1 
        print "HDneg",HDneg

        d1fix = np.mean(d1[:-10])
        d2fix = np.mean(d2[:-10])
        DelMSN = d1fix - d2fix
        DelGpi = (D*d1fix) + ((IDpos - IDneg)*d2fix)

    return (DelMSN,DelGpi)
paramsearchGA_DopDep_nonlinear.py 文件源码 项目:Homology_BG 作者: jyotikab 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def diffD1D2(Flags):
    # To check if the difference between D1 and D2 amplifies downstream
    # First decide which model to use
    delay = 1
    if Flags == "Allsym":
        (d1,d2,fsi,ti,ta,stn,gpi,ipctx,A,B,params) = calcRates(Flags,delay)
        # D = Direct pathway, ID = Indirect pathway, HD = Hyperdirect pathway
        # Reducing a full recurrent matrix leads to postive and negative contributions in ID and HD instead of pure just positive contributions
        D = params['gpid1']
        print "Direct",D

        de1 = 1. + (params['d1d1']*params['fsifsi']) - (params['stnti']*params['stnstn'])
        Ex1 = (params['stnti']*params['d1d1']*params['fsifsi']*params['gpistn'])/de1        
        Ex2 = (params['gpid1']*params['stnstn']*params['d1ta']*params['fsifsi'])/de1        
        IDpos = params['stnti']*params['tid2']*(params['d1ta']*params['gpid1']*params['tistn']+ Ex1 + Ex2)

        print "IDpos",IDpos

        Ex3 = (params['d1ta']+params['d1ta']*params['tata']+((params['stnta']*params['fsiti']*params['d1fsi'])/de1))
        IDneg = params['gpid1']+params['gpiti']*params['tid2']+params['gpid1']*params['stnstn']*params['tid2']*Ex3  
        print "IDneg",IDneg

        HDpos = (params['jstnctx']*params['gpid1']*params['stnstn']*params['fsiti']*params['d1fsi'])/de1
        print "HDpos",HDpos
        Ex4 = params['jstnctx']*params['d1d1']*params['fsifsi']*params['stnti']*params['gpistn']
        Ex5 = params['jstnctx']*params['gpid1']*params['stnstn']*params['d1ta']*params['fsifsi']
        HDneg = (Ex4 + Ex5)/de1 
        print "HDneg",HDneg

        d1fix = np.mean(d1[:-10])
        d2fix = np.mean(d2[:-10])
        DelMSN = d1fix - d2fix
        DelGpi = (D*d1fix) + ((IDpos - IDneg)*d2fix)

    return (DelMSN,DelGpi)
paramsearchGA_DopDep_nonlinear.py 文件源码 项目:Homology_BG 作者: jyotikab 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def diffD1D2(Flags):
    # To check if the difference between D1 and D2 amplifies downstream
    # First decide which model to use
    delay = 1
    if Flags == "Allsym":
        (d1,d2,fsi,ti,ta,stn,gpi,ipctx,A,B,params) = calcRates(Flags,delay)
        # D = Direct pathway, ID = Indirect pathway, HD = Hyperdirect pathway
        # Reducing a full recurrent matrix leads to postive and negative contributions in ID and HD instead of pure just positive contributions
        D = params['gpid1']
        print "Direct",D

        de1 = 1. + (params['d1d1']*params['fsifsi']) - (params['stnti']*params['stnstn'])
        Ex1 = (params['stnti']*params['d1d1']*params['fsifsi']*params['gpistn'])/de1        
        Ex2 = (params['gpid1']*params['stnstn']*params['d1ta']*params['fsifsi'])/de1        
        IDpos = params['stnti']*params['tid2']*(params['d1ta']*params['gpid1']*params['tistn']+ Ex1 + Ex2)

        print "IDpos",IDpos

        Ex3 = (params['d1ta']+params['d1ta']*params['tata']+((params['stnta']*params['fsiti']*params['d1fsi'])/de1))
        IDneg = params['gpid1']+params['gpiti']*params['tid2']+params['gpid1']*params['stnstn']*params['tid2']*Ex3  
        print "IDneg",IDneg

        HDpos = (params['jstnctx']*params['gpid1']*params['stnstn']*params['fsiti']*params['d1fsi'])/de1
        print "HDpos",HDpos
        Ex4 = params['jstnctx']*params['d1d1']*params['fsifsi']*params['stnti']*params['gpistn']
        Ex5 = params['jstnctx']*params['gpid1']*params['stnstn']*params['d1ta']*params['fsifsi']
        HDneg = (Ex4 + Ex5)/de1 
        print "HDneg",HDneg

        d1fix = np.mean(d1[:-10])
        d2fix = np.mean(d2[:-10])
        DelMSN = d1fix - d2fix
        DelGpi = (D*d1fix) + ((IDpos - IDneg)*d2fix)

    return (DelMSN,DelGpi)
paramsearchGA_DopDep_nonlinear.py 文件源码 项目:Homology_BG 作者: jyotikab 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def diffD1D2(Flags):
    # To check if the difference between D1 and D2 amplifies downstream
    # First decide which model to use
    delay = 1
    if Flags == "Allsym":
        (d1,d2,fsi,ti,ta,stn,gpi,ipctx,A,B,params) = calcRates(Flags,delay)
        # D = Direct pathway, ID = Indirect pathway, HD = Hyperdirect pathway
        # Reducing a full recurrent matrix leads to postive and negative contributions in ID and HD instead of pure just positive contributions
        D = params['gpid1']
        print "Direct",D

        de1 = 1. + (params['d1d1']*params['fsifsi']) - (params['stnti']*params['stnstn'])
        Ex1 = (params['stnti']*params['d1d1']*params['fsifsi']*params['gpistn'])/de1        
        Ex2 = (params['gpid1']*params['stnstn']*params['d1ta']*params['fsifsi'])/de1        
        IDpos = params['stnti']*params['tid2']*(params['d1ta']*params['gpid1']*params['tistn']+ Ex1 + Ex2)

        print "IDpos",IDpos

        Ex3 = (params['d1ta']+params['d1ta']*params['tata']+((params['stnta']*params['fsiti']*params['d1fsi'])/de1))
        IDneg = params['gpid1']+params['gpiti']*params['tid2']+params['gpid1']*params['stnstn']*params['tid2']*Ex3  
        print "IDneg",IDneg

        HDpos = (params['jstnctx']*params['gpid1']*params['stnstn']*params['fsiti']*params['d1fsi'])/de1
        print "HDpos",HDpos
        Ex4 = params['jstnctx']*params['d1d1']*params['fsifsi']*params['stnti']*params['gpistn']
        Ex5 = params['jstnctx']*params['gpid1']*params['stnstn']*params['d1ta']*params['fsifsi']
        HDneg = (Ex4 + Ex5)/de1 
        print "HDneg",HDneg

        d1fix = np.mean(d1[:-10])
        d2fix = np.mean(d2[:-10])
        DelMSN = d1fix - d2fix
        DelGpi = (D*d1fix) + ((IDpos - IDneg)*d2fix)

    return (DelMSN,DelGpi)


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