Python numpy.matrix() 使用实例

The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

Example 1

def transformation_from_points(points1, points2):
    points1 = points1.astype(np.float64)
    points2 = points2.astype(np.float64)

    c1 = np.mean(points1, axis=0)
    c2 = np.mean(points2, axis=0)
    points1 -= c1
    points2 -= c2

    s1 = np.std(points1)
    s2 = np.std(points2)
    points1 /= s1
    points2 /= s2

    U, S, Vt = np.linalg.svd(points1.T * points2)
    R = (U * Vt).T

    return np.vstack([np.hstack(((s2 / s1) * R,
                                       c2.T - (s2 / s1) * R * c1.T)),
                         np.matrix([0., 0., 1.])]) 

Example 2

def KeyGen(n, m, k, d, q):
	'''
		input:
		q : polynomial size prime number
		n, m, k : dimensions specifiers
		d : SIS parameter, hardest instances are where d ~ q^(n/m)

		output:
		Signing Key S :  Matrix of dimension mxk with coefficients in [-d.d]
		Verification Key A : Matrix of dimension nxm with coefficients from [-(q-1)/2,(q-1)/2]
		T : the matrix AS ,it is used in the Verification of the signature

	'''
	S = crypt_secure_matrix(d, m, k)
	A = crypt_secure_matrix((q-1)/2, n, m)
	T = np.matmul(A, S)
	return S, A, T 

Example 3

def test_calculate_slopes_no_mask():
    mat = np.matrix([[1, 5, 7, 0, 12, 14],
                     [12, 5, 7, 18, 0, 0],
                     [6, 2, 9, 17, 0, 5]])
    tp = np.array([0, 5, 7, 11, 15, 19])
    mask = np.array(['1', '1', '1', '1', '1', '1'])
    res = calculate_slopes(mat, tp, mask)
    true_res = np.matrix([[0.8, 1, -1.75, 3, 0.5],
                          [-1.4, 1, 2.75, -4.5, 0],
                          [-0.8, 3.5, 2, -4.25, 1.25]])
    assert (res - true_res).sum() <= 10e-10

#def generate_STS_distance_matrix(slope_matrix, nthreads)
#def test_generate_STS_distance_matrix():
#    pass

#def sts_matrix_generator(ind, slope_matrix)
#def test_sts_matrix_generator():
#    pass

#def cluster_dbscan(dist_matrix, eps=1, distance_measure="sts")
#def test_cluster_dbscan():
#    pass

#def zscore(x) 

Example 4

def timeseriesdata_constructor_existing_file():
    """Tests the TimeSeriesData class constructor when the file exists. Tests
    that the data are as expected, and that the filled data flag is set."""
    tsd = TimeSeriesData("./tests/data/small.h5")
    assert tsd.filled_data
    #Validate that the time-series matrix data are correct
    dat = np.matrix([[0, 2, 0, 0],
                     [2, 1, 3, 4],
                     [1, 1, 1, 1],
                     [0, 0, 1, 0]])
    assert (dat == tsd.get_sparse_matrix().todense()).all()
    assert (tsd.get_time_points() == [0, 4, 10, 12]).all()
    sequence_ids = np.array([b'9247ec5fd33e99387d41e8fc0d7ee278',
                             b'53f74905f7826fee79dd09b0c12caddf',
                             b'8829fefe91ead511a30df118060b1030',
                             b'7f97b4991342bf629966aeac0708c94f'])
    assert (sequence_ids == tsd.get_array_by_chunks("genes/sequenceids")).all()
    sample_names = np.array([b'72c', b'29', b'qpa', b'15'])
    assert (sample_names == tsd.get_array_by_chunks("samples/names")).all()

# __del__(self)
#Test if HDF5 file closed 

Example 5

def gradientDescent(X, y, theta, alpha, iters):
    temp = np.matrix(np.zeros(theta.shape))
    params = int(theta.ravel().shape[1]) #flattens
    cost = np.zeros(iters)

    for i in range(iters):
        err = (X * theta.T) - y
        
        for j in range(params):
            term = np.multiply(err, X[:,j])
            temp[0, j] = theta[0, j] - ((alpha / len(X)) * np.sum(term))
        
        theta = temp
        cost[i] = computeCost(X, y, theta)
    
    return theta, cost 

Example 6

def computeCost(X, y, theta):
    inner = np.power(((X * theta.T) - y), 2)
    return np.sum(inner) / (2 * len(X))

#def gradientDescent(X, y, theta, alpha, iters):
#    temp = np.matrix(np.zeros(theta.shape))
#    params = int(theta.ravel().shape[1]) #flattens
#    cost = np.zeros(iters)
#
#    for i in range(iters):
#        err = (X * theta.T) - y
#        
#        for j in range(params):
#            term = np.multiply(err, X[:,j])
#            temp[0, j] = theta[0, j] - ((alpha / len(X)) * np.sum(term))
#        
#        theta = temp
#        cost[i] = computeCost(X, y, theta)
#    
#    return theta, cost 

Example 7

def adapt_z_state(self,main_rdd, cinfo,beta):
        ainv = cinfo
        def Updatez(tpl):
            tt=[]
            for ((tx,lam,state,z),index) in tpl:
                random.setstate(state)
                p = random.random()
                state = random.getstate()
                if p<self.ptr:
                    znew=float(-np.matrix(tx)*ainv*np.matrix(tx).T)
                else:
                    znew=0.0
                z=(1-beta)*z+beta*znew
                tt.append(((tx,lam,state,z),index))
            return tt
        main_rdd = main_rdd.mapValues(Updatez).cache()
        return main_rdd 

Example 8

def update_lam_z(self, main_rdd, cinfo,iStar,Gamma):
        ainv = cinfo
        def update(t, ainv):
            p=[]
            [tpl, gen] = t
            for ((tx,lam,z),index) in tpl:
                znew=0.0
                if gen.random()<self.ptr:
                    znew=-(np.matrix(tx)*ainv*np.matrix(tx).T)[0,0]
                else:
                    znew= 0.0
                zupdtd = (1.0-self.beta)*z + self.beta*znew
                if index != iStar:
                    lamupdt = (1-Gamma)*lam
                else:
                    lamupdt = (1-Gamma)*lam +lam
                p.append(((tx,lamupdt,zupdtd),index))
            out = [p,gen]
            return out
               
        main_rdd = main_rdd.mapValues(lambda t:update(t,ainv)).persist()
        return main_rdd 

Example 9

def update_lam_z(self, main_rdd, cinfo,iStar,Gamma):
        ainv,ainv2 = cinfo
        def update(t, ainv2):
            p=[]
            [tpl, gen] = t
            for ((tx,lam,z),index) in tpl:
                znew=0.0
                if gen.random()<self.ptr:
                    znew=-(np.matrix(tx)*ainv2*np.matrix(tx).T)[0,0]
                else:
                    znew= 0.0
                zupdtd = (1.0-self.beta)*z + self.beta*znew
                if index != iStar:
                    lamupdt = (1-Gamma)*lam
                else:
                    lamupdt = (1-Gamma)*lam +lam
                p.append(((tx,lamupdt,zupdtd),index))
            out = [p,gen]
            return out

        main_rdd = main_rdd.mapValues(lambda t:update(t,ainv2)).persist()
        return main_rdd 

Example 10

def update_comm_info(self,cinfo,iStar,mingrad,txmin,Gamma):
        def UpdateAinv2(binv2,u,v,UVT,Denom,alpha,Xi):
             return binv2-alpha*UVT/Denom-alpha*UVT.T/Denom+alpha**2*UVT*Xi*u.T/Denom**2
        def UpdateAinv3(binv3,U1,U2,U3,D,Xi,Gamma):
            return binv3-Gamma*U2*U2.T/D-Gamma*U1*U3.T/D-Gamma*U3*U1.T/D+Gamma**2*U1*U2.T*Xi*U2.T/D**2+Gamma**2*U2*U2.T*Xi*U1.T/D**2+Gamma**2*U1*U3.T*Xi*U1.T/D**2-Gamma**3*U1*U2.T*Xi*U2.T*Xi*U1.T/D**3     
        ainv,ainv2,ainv3=cinfo
        binv=1.0/(1.0-Gamma)*ainv
        binv2=1.0/(1.0-Gamma)**2*ainv2
        binv3=1.0/(1.0-Gamma)**3*ainv3
        u1=binv*np.matrix(txmin).T
        u2=binv2*np.matrix(txmin).T
        u3=binv3*np.matrix(txmin).T
        UVT=u1*u2.T
        Denom=1+Gamma*u1.T*np.matrix(txmin).T       
        ainv=rankOneInvUpdate(binv,Gamma*np.matrix(txmin).T,np.matrix(txmin).T)
        ainv2=UpdateAinv2(binv2,u1,u2,UVT,Denom,Gamma,np.matrix(txmin).T)
        ainv3=UpdateAinv3(binv3,u1,u2,u3,Denom,np.matrix(txmin).T,Gamma)
        return ainv,ainv2,ainv3 

Example 11

def compute_mingrad_l1(self,main_rdd,cinfo,K):
        R = cinfo
        def maxmin_l1(tpl1,tpl2):
            (z1,x1,lam1,i1)=tpl1
            (z2,x2,lam2,i2)=tpl2
            zt = max(abs(z1),abs(z2))
            if zt>abs(z2):
                out = (z1,x1,lam1,i1)
            else:
                out = (z2,x2,lam2,i2)
            return out
                    
        def CompMingrad(tpl):
            p=[]
            for ((tx,lam),index) in tpl:
                p.append(((np.matrix(tx)*R)[0,0],tx,lam,index))

            return p
        (mingrad,xmin,lambdaMin,iStar)=main_rdd.flatMapValues(CompMingrad).map(lambda (key, value):value).reduce(maxmin_l1)
        s_star = -np.sign(mingrad)
        return (mingrad,xmin,lambdaMin,iStar,s_star) 

Example 12

def update_lam_z(self, main_rdd, cinfo,iStar,Gamma):
        R = cinfo
        def update(t, R):
            p=[]
            [tpl, gen] = t
            for ((tx,lam,z),index) in tpl:
                znew=0.0
                if gen.random()<self.ptr:
                    znew=(2*np.matrix(tx)*R)[0,0]
                else:
                    znew= 0.0
                zupdtd = (1.0-self.beta)*z + self.beta*znew
                if index != iStar:
                    lamupdt = (1-Gamma)*lam
                else:
                    lamupdt = (1-Gamma)*lam +lam
                p.append(((tx,lamupdt,zupdtd),index))
            out = [p,gen]
            return out

        main_rdd = main_rdd.mapValues(lambda t:update(t,R)).persist()
        return main_rdd 

Example 13

def  gen_comm_info(self,main_rdd):
        def cominfo(tpl):
            p=[]
            for ((tx,lam),index) in tpl:
                p.append(np.matrix(tx).T*lam)
            return p    
        def findDim(tpl):
            for ((tx,lam),index) in tpl:
                d = len(tx)
            return d
        d = main_rdd.mapValues(findDim).values().reduce(lambda x,y:x)
        c=main_rdd.flatMapValues(cominfo).map(lambda (key,value):value).reduce(lambda x,y:x+y)
        V=matrix(0.0,(d,1))
        for j in range(d):
            V[j]=math.exp(-self.C*self.r[j]*c[j,0])    
        return d,V 

Example 14

def computegap(self,cinfo,main_rdd,iStar,mingrad,lambdaMin):
        d,V = cinfo
        z=matrix(0.0,(d,1))
        vSum=float(np.sum(V))
        for j in range(d):
            z[j]=-V[j]*self.C*self.r[j]/vSum
        def CompGap(tpl,lambdaMin,mingrad,iStar):
            p=[]
            for ((tx,lam),index) in tpl:
                if index!=iStar:
                    p.append((np.matrix(tx)*np.matrix(z))[0,0]*lam)
                else:
                    p.append((lambdaMin-1)*mingrad)
            return p   
        gap=main_rdd.flatMapValues(lambda tpl:CompGap(tpl,lambdaMin,mingrad,iStar)).map(lambda (key, value):value).reduce(lambda x,y:x+y)
        return gap 

Example 15

def get_landmarks(self,im,fname,n=2):
        '''
        ??????????????????????????????
        im:
            ???numpy??
        fname:
            ????????
        ???:
            ????(x,y)?????
        '''
        rects = self.detector(im, 1)
        
        if len(rects) >=5:
            raise TooManyFaces('Too many faces in '+fname)
        if len(rects) <2:
            raise NoFace('No enough face in' +fname)
        return [np.matrix([[p.x, p.y] for p in self.predictor(im, rect).parts()]) for rect in rects] 

Example 16

def get_landmarks(self,im,fname,n=1):
        '''
        ??????????????????????????????
        im:
            ???numpy??
        fname:
            ????????
        ???:
            ????(x,y)?????
        '''
        rects = self.detector(im, 1)
        
        if len(rects) > n:
            raise TooManyFaces('No face in '+fname)
        if len(rects) < 0:
            raise NoFace('Too many faces in '+fname)
        return np.matrix([[p.x, p.y] for p in self.predictor(im, rects[0]).parts()]) 

Example 17

def __init__(self, originFilename):
		self._originFilename = originFilename
		if originFilename is None:
			self._name = 'None'
		else:
			self._name = os.path.basename(originFilename)
		if '.' in self._name:
			self._name = os.path.splitext(self._name)[0]
		self._meshList = []
		self._position = numpy.array([0.0, 0.0])
		self._matrix = numpy.matrix([[1,0,0],[0,1,0],[0,0,1]], numpy.float64)
		self._transformedMin = None
		self._transformedMax = None
		self._transformedSize = None
		self._boundaryCircleSize = None
		self._drawOffset = None
		self._boundaryHull = None
		self._printAreaExtend = numpy.array([[-1,-1],[ 1,-1],[ 1, 1],[-1, 1]], numpy.float32)
		self._headAreaExtend = numpy.array([[-1,-1],[ 1,-1],[ 1, 1],[-1, 1]], numpy.float32)
		self._headMinSize = numpy.array([1, 1], numpy.float32)
		self._printAreaHull = None
		self._headAreaHull = None
		self._headAreaMinHull = None

		self._loadAnim = None 

Example 18

def test_iter_allocate_output_subtype():
    # Make sure that the subtype with priority wins

    # matrix vs ndarray
    a = np.matrix([[1, 2], [3, 4]])
    b = np.arange(4).reshape(2, 2).T
    i = nditer([a, b, None], [],
                    [['readonly'], ['readonly'], ['writeonly', 'allocate']])
    assert_equal(type(a), type(i.operands[2]))
    assert_(type(b) != type(i.operands[2]))
    assert_equal(i.operands[2].shape, (2, 2))

    # matrix always wants things to be 2D
    b = np.arange(4).reshape(1, 2, 2)
    assert_raises(RuntimeError, nditer, [a, b, None], [],
                    [['readonly'], ['readonly'], ['writeonly', 'allocate']])
    # but if subtypes are disabled, the result can still work
    i = nditer([a, b, None], [],
            [['readonly'], ['readonly'], ['writeonly', 'allocate', 'no_subtype']])
    assert_equal(type(b), type(i.operands[2]))
    assert_(type(a) != type(i.operands[2]))
    assert_equal(i.operands[2].shape, (1, 2, 2)) 

Example 19

def test_shapes(self):
        dims = [
            ((1, 1), (2, 1, 1)),     # broadcast first argument
            ((2, 1, 1), (1, 1)),     # broadcast second argument
            ((2, 1, 1), (2, 1, 1)),  # matrix stack sizes match
            ]

        for dt, (dm1, dm2) in itertools.product(self.types, dims):
            a = np.ones(dm1, dtype=dt)
            b = np.ones(dm2, dtype=dt)
            res = self.matmul(a, b)
            assert_(res.shape == (2, 1, 1))

        # vector vector returns scalars.
        for dt in self.types:
            a = np.ones((2,), dtype=dt)
            b = np.ones((2,), dtype=dt)
            c = self.matmul(a, b)
            assert_(np.array(c).shape == ()) 

Example 20

def test_inner_product_with_various_contiguities(self):
        # github issue 6532
        for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
            # check an inner product involving a matrix transpose
            A = np.array([[1, 2], [3, 4]], dtype=dt)
            B = np.array([[1, 3], [2, 4]], dtype=dt)
            C = np.array([1, 1], dtype=dt)
            desired = np.array([4, 6], dtype=dt)
            assert_equal(np.inner(A.T, C), desired)
            assert_equal(np.inner(C, A.T), desired)
            assert_equal(np.inner(B, C), desired)
            assert_equal(np.inner(C, B), desired)
            # check a matrix product
            desired = np.array([[7, 10], [15, 22]], dtype=dt)
            assert_equal(np.inner(A, B), desired)
            # check the syrk vs. gemm paths
            desired = np.array([[5, 11], [11, 25]], dtype=dt)
            assert_equal(np.inner(A, A), desired)
            assert_equal(np.inner(A, A.copy()), desired)
            # check an inner product involving an aliased and reversed view
            a = np.arange(5).astype(dt)
            b = a[::-1]
            desired = np.array(10, dtype=dt).item()
            assert_equal(np.inner(b, a), desired) 

Example 21

def test_basic(self):
        y1 = np.array([1, 2, 3])
        assert_(average(y1, axis=0) == 2.)
        y2 = np.array([1., 2., 3.])
        assert_(average(y2, axis=0) == 2.)
        y3 = [0., 0., 0.]
        assert_(average(y3, axis=0) == 0.)

        y4 = np.ones((4, 4))
        y4[0, 1] = 0
        y4[1, 0] = 2
        assert_almost_equal(y4.mean(0), average(y4, 0))
        assert_almost_equal(y4.mean(1), average(y4, 1))

        y5 = rand(5, 5)
        assert_almost_equal(y5.mean(0), average(y5, 0))
        assert_almost_equal(y5.mean(1), average(y5, 1))

        y6 = np.matrix(rand(5, 5))
        assert_array_equal(y6.mean(0), average(y6, 0)) 

Example 22

def test_return_type(self):
        a = np.ones([2, 2])
        m = np.asmatrix(a)
        assert_equal(type(kron(a, a)), np.ndarray)
        assert_equal(type(kron(m, m)), np.matrix)
        assert_equal(type(kron(a, m)), np.matrix)
        assert_equal(type(kron(m, a)), np.matrix)

        class myarray(np.ndarray):
            __array_priority__ = 0.0

        ma = myarray(a.shape, a.dtype, a.data)
        assert_equal(type(kron(a, a)), np.ndarray)
        assert_equal(type(kron(ma, ma)), myarray)
        assert_equal(type(kron(a, ma)), np.ndarray)
        assert_equal(type(kron(ma, a)), myarray) 

Example 23

def test_allany_onmatrices(self):
        x = np.array([[0.13, 0.26, 0.90],
                      [0.28, 0.33, 0.63],
                      [0.31, 0.87, 0.70]])
        X = np.matrix(x)
        m = np.array([[True, False, False],
                      [False, False, False],
                      [True, True, False]], dtype=np.bool_)
        mX = masked_array(X, mask=m)
        mXbig = (mX > 0.5)
        mXsmall = (mX < 0.5)

        self.assertFalse(mXbig.all())
        self.assertTrue(mXbig.any())
        assert_equal(mXbig.all(0), np.matrix([False, False, True]))
        assert_equal(mXbig.all(1), np.matrix([False, False, True]).T)
        assert_equal(mXbig.any(0), np.matrix([False, False, True]))
        assert_equal(mXbig.any(1), np.matrix([True, True, True]).T)

        self.assertFalse(mXsmall.all())
        self.assertTrue(mXsmall.any())
        assert_equal(mXsmall.all(0), np.matrix([True, True, False]))
        assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T)
        assert_equal(mXsmall.any(0), np.matrix([True, True, False]))
        assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T) 

Example 24

def test_compressed(self):
        # Tests compressed
        a = array([1, 2, 3, 4], mask=[0, 0, 0, 0])
        b = a.compressed()
        assert_equal(b, a)
        a[0] = masked
        b = a.compressed()
        assert_equal(b, [2, 3, 4])

        a = array(np.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0])
        b = a.compressed()
        assert_equal(b, a)
        self.assertTrue(isinstance(b, np.matrix))
        a[0, 0] = masked
        b = a.compressed()
        assert_equal(b, [[2, 3, 4]]) 

Example 25

def test_ravel(self):
        # Tests ravel
        a = array([[1, 2, 3, 4, 5]], mask=[[0, 1, 0, 0, 0]])
        aravel = a.ravel()
        assert_equal(aravel._mask.shape, aravel.shape)
        a = array([0, 0], mask=[1, 1])
        aravel = a.ravel()
        assert_equal(aravel._mask.shape, a.shape)
        a = array(np.matrix([1, 2, 3, 4, 5]), mask=[[0, 1, 0, 0, 0]])
        aravel = a.ravel()
        assert_equal(aravel.shape, (1, 5))
        assert_equal(aravel._mask.shape, a.shape)
        # Checks that small_mask is preserved
        a = array([1, 2, 3, 4], mask=[0, 0, 0, 0], shrink=False)
        assert_equal(a.ravel()._mask, [0, 0, 0, 0])
        # Test that the fill_value is preserved
        a.fill_value = -99
        a.shape = (2, 2)
        ar = a.ravel()
        assert_equal(ar._mask, [0, 0, 0, 0])
        assert_equal(ar._data, [1, 2, 3, 4])
        assert_equal(ar.fill_value, -99)
        # Test index ordering
        assert_equal(a.ravel(order='C'), [1, 2, 3, 4])
        assert_equal(a.ravel(order='F'), [1, 3, 2, 4]) 

Example 26

def test_view(self):
        # Test view w/ flexible dtype
        iterator = list(zip(np.arange(10), np.random.rand(10)))
        data = np.array(iterator)
        a = array(iterator, dtype=[('a', float), ('b', float)])
        a.mask[0] = (1, 0)
        controlmask = np.array([1] + 19 * [0], dtype=bool)
        # Transform globally to simple dtype
        test = a.view(float)
        assert_equal(test, data.ravel())
        assert_equal(test.mask, controlmask)
        # Transform globally to dty
        test = a.view((float, 2))
        assert_equal(test, data)
        assert_equal(test.mask, controlmask.reshape(-1, 2))

        test = a.view((float, 2), np.matrix)
        assert_equal(test, data)
        self.assertTrue(isinstance(test, np.matrix)) 

Example 27

def dot_generalized(a, b):
    a = asarray(a)
    if a.ndim >= 3:
        if a.ndim == b.ndim:
            # matrix x matrix
            new_shape = a.shape[:-1] + b.shape[-1:]
        elif a.ndim == b.ndim + 1:
            # matrix x vector
            new_shape = a.shape[:-1]
        else:
            raise ValueError("Not implemented...")
        r = np.empty(new_shape, dtype=np.common_type(a, b))
        for c in itertools.product(*map(range, a.shape[:-2])):
            r[c] = dot(a[c], b[c])
        return r
    else:
        return dot(a, b) 

Example 28

def do(self, a, b):
        arr = np.asarray(a)
        m, n = arr.shape
        u, s, vt = linalg.svd(a, 0)
        x, residuals, rank, sv = linalg.lstsq(a, b)
        if m <= n:
            assert_almost_equal(b, dot(a, x))
            assert_equal(rank, m)
        else:
            assert_equal(rank, n)
        assert_almost_equal(sv, sv.__array_wrap__(s))
        if rank == n and m > n:
            expect_resids = (
                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
            expect_resids = np.asarray(expect_resids)
            if len(np.asarray(b).shape) == 1:
                expect_resids.shape = (1,)
                assert_equal(residuals.shape, expect_resids.shape)
        else:
            expect_resids = np.array([]).view(type(x))
        assert_almost_equal(residuals, expect_resids)
        assert_(np.issubdtype(residuals.dtype, np.floating))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
        assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) 

Example 29

def test_bad_args(self):
        # Check that bad arguments raise the appropriate exceptions.

        A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
        B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)

        # Using `axis=<integer>` or passing in a 1-D array implies vector
        # norms are being computed, so also using `ord='fro'`
        # or `ord='nuc'` raises a ValueError.
        assert_raises(ValueError, norm, A, 'fro', 0)
        assert_raises(ValueError, norm, A, 'nuc', 0)
        assert_raises(ValueError, norm, [3, 4], 'fro', None)
        assert_raises(ValueError, norm, [3, 4], 'nuc', None)

        # Similarly, norm should raise an exception when ord is any finite
        # number other than 1, 2, -1 or -2 when computing matrix norms.
        for order in [0, 3]:
            assert_raises(ValueError, norm, A, order, None)
            assert_raises(ValueError, norm, A, order, (0, 1))
            assert_raises(ValueError, norm, B, order, (1, 2))

        # Invalid axis
        assert_raises(ValueError, norm, B, None, 3)
        assert_raises(ValueError, norm, B, None, (2, 3))
        assert_raises(ValueError, norm, B, None, (0, 1, 2)) 

Example 30

def test_matrix_rank(self):
        # Full rank matrix
        yield assert_equal, 4, matrix_rank(np.eye(4))
        # rank deficient matrix
        I = np.eye(4)
        I[-1, -1] = 0.
        yield assert_equal, matrix_rank(I), 3
        # All zeros - zero rank
        yield assert_equal, matrix_rank(np.zeros((4, 4))), 0
        # 1 dimension - rank 1 unless all 0
        yield assert_equal, matrix_rank([1, 0, 0, 0]), 1
        yield assert_equal, matrix_rank(np.zeros((4,))), 0
        # accepts array-like
        yield assert_equal, matrix_rank([1]), 1
        # greater than 2 dimensions raises error
        yield assert_raises, TypeError, matrix_rank, np.zeros((2, 2, 2))
        # works on scalar
        yield assert_equal, matrix_rank(1), 1 

Example 31

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)) 

Example 32

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)) 

Example 33

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)) 

Example 34

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))) 

Example 35

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]]) 

Example 36

def vector3(x, y, z):
	return np.matrix([[x],[y],[z]]) 

Example 37

def vector6(a, b, c, x, y, z):
	return np.matrix([[a],[b],[c],[x],[y],[z]]) 

Example 38

def col(v):
	col = [[x] for x in v]
	return np.matrix(col) 

Example 39

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
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 

Example 40

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
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 

Example 41

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

# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 

Example 42

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 

Example 43

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 

Example 44

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])]) 

Example 45

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) 

Example 46

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 

Example 47

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) 

Example 48

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) 

Example 49

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) 

Example 50

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) 
点赞

发表评论

电子邮件地址不会被公开。 必填项已用*标注