Python numpy.argsort方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def classical_mds(self, D):
        ''' 
        Classical multidimensional scaling

        Parameters
        ----------
        D : square 2D ndarray
            Euclidean Distance Matrix (matrix containing squared distances between points
        '''

        # Apply MDS algorithm for denoising
        n = D.shape[0]
        J = np.eye(n) - np.ones((n,n))/float(n)
        G = -0.5*np.dot(J, np.dot(D, J))

        s, U = np.linalg.eig(G)

        # we need to sort the eigenvalues in decreasing order
        s = np.real(s)
        o = np.argsort(s)
        s = s[o[::-1]]
        U = U[:,o[::-1]]

        S = np.diag(s)[0:self.dim,:]
        self.X = np.dot(np.sqrt(S),U.T) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def _peaks1D(self):
        if self.num_src == 1:
            self.src_idx[0] = np.argmax(self.P)
            self.sources[:, 0] = self.loc[:, self.src_idx[0]]
            self.phi_recon = self.theta[self.src_idx[0]]
        else:
            peak_idx = []
            n = self.P.shape[0]
            for i in range(self.num_loc):
                # straightforward peak finding
                if self.P[i] >= self.P[(i-1)%n] and self.P[i] > self.P[(i+1)%n]:
                    if len(peak_idx) == 0 or peak_idx[-1] != i-1:
                        if not (i == self.num_loc and self.P[i] == self.P[0]):
                            peak_idx.append(i)

            peaks = self.P[peak_idx]
            max_idx = np.argsort(peaks)[-self.num_src:]
            self.src_idx = [peak_idx[k] for k in max_idx]
            self.sources = self.loc[:, self.src_idx]
            self.phi_recon = self.theta[self.src_idx]
            self.num_src = len(self.src_idx)


# ------------------Miscellaneous Functions---------------------# 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def cmap(self, data=None):
        '''
        lblidx.cmap() yields a colormap for the given label index object that assumes that the data
          being plotted will be rescaled such that label 0 is 0 and the highest label value in the
          label index is equal to 1.
        lblidx.cmap(data) yields a colormap that will correctly color the labels given in data if
          data is scaled such that its minimum and maximum value are 0 and 1.
        '''
        import matplotlib.colors
        from_list = matplotlib.colors.LinearSegmentedColormap.from_list
        if data is None: return self.colormap
        data = np.asarray(data).flatten()
        (vmin,vmax) = (np.min(data), np.max(data))
        ii  = np.argsort(self.ids)
        ids = np.asarray(self.ids)[ii]
        if vmin == vmax:
            (vmin,vmax,ii) = (vmin-0.5, vmax+0.5, vmin)
            clr = self.color_lookup(ii)
            return from_list('label1', [(0, clr), (1, clr)])
        q   = (ids >= vmin) & (ids <= vmax)
        ids = ids[q]
        clrs = self.color_lookup(ids)
        vals = (ids - vmin) / (vmax - vmin)
        return from_list('label%d' % len(vals), list(zip(vals, clrs))) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def k_nearest_neighbor(self, sequence):
        # Calculate dist_matrix
        dist_array = pdist(sequence)
        dist_matrix = squareform(dist_array)
        # Construct tour
        new_sequence = [sequence[0]]
        current_city = 0
        visited_cities = [0]
        for i in range(1,len(sequence)):
            j = np.random.randint(0,min(len(sequence)-i,self.kNN))
            next_city = [index for index in dist_matrix[current_city].argsort() if index not in visited_cities][j]
            visited_cities.append(next_city)
            new_sequence.append(sequence[next_city])
            current_city = next_city
        return np.asarray(new_sequence)


    # Generate random TSP-TW instance 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def about0(ENGINE, rang=5, recur=100, refine=True, explore=False):
    ENGINE.set_groups_as_molecules()
    [g.set_move_generator(RotationAboutSymmetryAxisGenerator(axis=0, amplitude=180)) for g in ENGINE.groups]
    # set selector
    centers   = [np.sum(ENGINE.realCoordinates[g.indexes], axis=0)/len(g) for g in ENGINE.groups]
    distances = [np.sqrt(np.add.reduce(c**2)) for c in centers]
    order     = np.argsort(distances)
    gs = RecursiveGroupSelector(DefinedOrderSelector(ENGINE, order = order ), recur=recur, refine=refine, explore=explore)
    ENGINE.set_group_selector(gs)
    # number of steps
    nsteps = recur*len(ENGINE.groups)
    for stepIdx in range(rang):
        LOGGER.info("Running 'about0' mode step %i"%(stepIdx))
        ENGINE.run(numberOfSteps=nsteps, saveFrequency=nsteps)

# ############ RUN ROTATION ABOUT SYMM AXIS 1 ############ # 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def about1(ENGINE, rang=5, recur=10, refine=True, explore=False):
    ENGINE.set_groups_as_molecules()
    [g.set_move_generator(RotationAboutSymmetryAxisGenerator(axis=1, amplitude=180)) for g in ENGINE.groups]
    # set selector
    centers   = [np.sum(ENGINE.realCoordinates[g.indexes], axis=0)/len(g) for g in ENGINE.groups]
    distances = [np.sqrt(np.add.reduce(c**2)) for c in centers]
    order     = np.argsort(distances)
    gs = RecursiveGroupSelector(DefinedOrderSelector(ENGINE, order = order ), recur=recur, refine=refine, explore=explore)
    ENGINE.set_group_selector(gs)
    # number of steps
    nsteps = recur*len(ENGINE.groups)
    for stepIdx in range(rang):
        LOGGER.info("Running 'about1' mode step %i"%(stepIdx))
        ENGINE.run(numberOfSteps=nsteps, saveFrequency=nsteps)

# ############ RUN ROTATION ABOUT SYMM AXIS 2 ############ # 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def about2(ENGINE, rang=5, recur=100, refine=True, explore=False):
    ENGINE.set_groups_as_molecules()
    [g.set_move_generator(RotationAboutSymmetryAxisGenerator(axis=2, amplitude=180)) for g in ENGINE.groups]
    # set selector
    centers   = [np.sum(ENGINE.realCoordinates[g.indexes], axis=0)/len(g) for g in ENGINE.groups]
    distances = [np.sqrt(np.add.reduce(c**2)) for c in centers]
    order     = np.argsort(distances)
    gs = RecursiveGroupSelector(DefinedOrderSelector(ENGINE, order = order ), recur=recur, refine=refine, explore=explore)
    ENGINE.set_group_selector(gs)
    # number of steps
    nsteps = recur*len(ENGINE.groups)
    for stepIdx in range(rang):
        LOGGER.info("Running 'about2' mode step %i"%(stepIdx))
        ENGINE.run(numberOfSteps=nsteps, saveFrequency=nsteps)

# ############ RUN TRANSLATION ALONG SYMM AXIS 0 ############ # 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def along0(ENGINE, rang=5, recur=100, refine=False, explore=True):
    ENGINE.set_groups_as_molecules()
    [g.set_move_generator(TranslationAlongSymmetryAxisGenerator(axis=0, amplitude=0.1)) for g in ENGINE.groups]
    # set selector
    centers   = [np.sum(ENGINE.realCoordinates[g.indexes], axis=0)/len(g) for g in ENGINE.groups]
    distances = [np.sqrt(np.add.reduce(c**2)) for c in centers]
    order     = np.argsort(distances)
    gs = RecursiveGroupSelector(DefinedOrderSelector(ENGINE, order = order ), recur=recur, refine=refine, explore=explore)
    ENGINE.set_group_selector(gs)
    # number of steps
    nsteps = recur*len(ENGINE.groups)
    for stepIdx in range(rang):
        LOGGER.info("Running 'along0' mode step %i"%(stepIdx))
        ENGINE.run(numberOfSteps=nsteps, saveFrequency=nsteps)

# ############ RUN TRANSLATION ALONG SYMM AXIS 1 ############ # 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def along2(ENGINE, rang=5, recur=100, refine=False, explore=True):
    ENGINE.set_groups_as_molecules()
    [g.set_move_generator(TranslationAlongSymmetryAxisGenerator(axis=2, amplitude=0.1)) for g in ENGINE.groups]
    # set selector
    centers   = [np.sum(ENGINE.realCoordinates[g.indexes], axis=0)/len(g) for g in ENGINE.groups]
    distances = [np.sqrt(np.add.reduce(c**2)) for c in centers]
    order     = np.argsort(distances)
    gs = RecursiveGroupSelector(DefinedOrderSelector(ENGINE, order = order ), recur=recur, refine=refine, explore=explore)
    ENGINE.set_group_selector(gs)
    # number of steps
    nsteps = recur*len(ENGINE.groups)
    for stepIdx in range(rang):
        LOGGER.info("Running 'along2' mode step %i"%(stepIdx))
        ENGINE.run(numberOfSteps=nsteps, saveFrequency=nsteps)

# ############ RUN MOLECULES ############ # 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def shrink(ENGINE, newDim):
    ENGINE.set_groups_as_molecules()
    [g.set_move_generator( MoveGeneratorCollector(collection=[TranslationGenerator(amplitude=0.2),RotationGenerator(amplitude=5)],randomize=True) ) for g in ENGINE.groups]
    # get groups order
    centers   = [np.sum(ENGINE.realCoordinates[g.indexes], axis=0)/len(g) for g in ENGINE.groups]
    distances = [np.sqrt(np.add.reduce(c**2)) for c in centers]
    order     = np.argsort(distances)
    # change boundary conditions
    bcFrom = str([list(bc) for bc in ENGINE.boundaryConditions.get_vectors()] )
    ENGINE.set_boundary_conditions(newDim)
    bcTo   = str([list(bc) for bc in ENGINE.boundaryConditions.get_vectors()] )
    LOGGER.info("boundary conditions changed from %s to %s"%(bcFrom,bcTo))
    # set selector
    recur = 200
    gs = RecursiveGroupSelector(DefinedOrderSelector(ENGINE, order = order ), recur=recur, refine=True)
    ENGINE.set_group_selector(gs)
    # number of steps
    nsteps = recur*len(ENGINE.groups)
    for stepIdx in range(10):
        LOGGER.info("Running 'shrink' mode step %i"%(stepIdx))
        ENGINE.run(numberOfSteps=nsteps, saveFrequency=nsteps)
        fname = "shrink_"+str(newDim).replace(".","p")

##########################################################################################
#####################################  RUN SIMULATION  ################################### 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def forward_ocr(self, img_):
        img_ = cv2.resize(img_, (80, 30))
        img_ = img_.transpose(1, 0)
        print(img_.shape)
        img_ = img_.reshape((1, 80, 30))
        print(img_.shape)
        # img_ = img_.reshape((80 * 30))
        img_ = np.multiply(img_, 1 / 255.0)
        self.predictor.forward(data=img_, **self.init_state_dict)
        prob = self.predictor.get_output(0)
        label_list = []
        for p in prob:
            print(np.argsort(p))
            max_index = np.argsort(p)[::-1][0]
            label_list.append(max_index)
        return self.__get_string(label_list) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def test_lwlr(self):
        # python -m unittest tests_regression.Tests_Regression.test_lwlr
        import locally_weighted_linear_regression as lwlr1
        from discomll.regression import locally_weighted_linear_regression as lwlr2

        x_train, y_train, x_test, y_test = datasets.regression_data()
        train_data, test_data = datasets.regression_data_discomll()

        lwlr1 = lwlr1.Locally_Weighted_Linear_Regression()
        taus = [1, 10, 25]
        sorted_indices = np.argsort([str(el) for el in x_test[:, 1].tolist()])

        for tau in taus:
            thetas1, estimation1 = lwlr1.fit(x_train, y_train, x_test, tau=tau)
            thetas1, estimation1 = np.array(thetas1)[sorted_indices], np.array(estimation1)[sorted_indices]

            results = lwlr2.fit_predict(train_data, test_data, tau=tau)
            thetas2, estimation2 = [], []

            for x_id, (est, thetas) in result_iterator(results):
                estimation2.append(est)
                thetas2.append(thetas)

            self.assertTrue(np.allclose(thetas1, thetas2, atol=1e-8))
            self.assertTrue(np.allclose(estimation1, estimation2, atol=1e-3)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def plot_rhodelta_rho(rho, delta):
	'''
	Plot scatter diagram for rho*delta_rho points

	Args:
		rho   : rho list
		delta : delta list
	'''
	logger.info("PLOT: rho*delta_rho plot")
	y=rho*delta
	r_index=np.argsort(-y)
	x=np.zeros(y.shape[0])
	idx=0
	for r in r_index:
	    x[r]=idx
	    idx+=1
	plt.figure(2)
	plt.clf()
	plt.scatter(x,y)
	plt.xlabel('sorted rho')
	plt.ylabel('rho*delta')
	plt.title("Decision Graph RhoDelta-Rho")
	plt.show()
	plt.savefig('Decision Graph RhoDelta-Rho.jpg') 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def calc_pr(gt, out, wt=None):
  if wt is None:
    wt = np.ones((gt.size,1))

  gt = gt.astype(np.float64).reshape((-1,1))
  wt = wt.astype(np.float64).reshape((-1,1))
  out = out.astype(np.float64).reshape((-1,1))

  gt = gt*wt
  tog = np.concatenate([gt, wt, out], axis=1)*1.
  ind = np.argsort(tog[:,2], axis=0)[::-1]
  tog = tog[ind,:]
  cumsumsortgt = np.cumsum(tog[:,0])
  cumsumsortwt = np.cumsum(tog[:,1])
  prec = cumsumsortgt / cumsumsortwt
  rec = cumsumsortgt / np.sum(tog[:,0])

  ap = voc_ap(rec, prec)
  return ap, rec, prec 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import argsort [as 别名]
def _get_room_dimensions(file_name, resolution, origin, flip=False):
  if fu.exists(file_name):
    a = utils.load_variables(file_name)['room_dimension']
    names = a.keys()
    dims = np.concatenate(a.values(), axis=0).reshape((-1,6))
    ind = np.argsort(names)
    dims = dims[ind,:]
    names = [names[x] for x in ind]
    if flip:
      dims_new = dims*1
      dims_new[:,1] = -dims[:,4]
      dims_new[:,4] = -dims[:,1]
      dims = dims_new*1

    dims = dims*100.
    dims[:,0] = dims[:,0] - origin[0]
    dims[:,1] = dims[:,1] - origin[1]
    dims[:,3] = dims[:,3] - origin[0]
    dims[:,4] = dims[:,4] - origin[1]
    dims = dims / resolution
    out = {'names': names, 'dims': dims}
  else:
    out = None
  return out