Python io.savemat方法代碼示例

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def make_mnist_data(path, isconv=False):
    X, Y = load_mnist(path, True)
    X = X.astype(np.float64)
    X2, Y2 = load_mnist(path, False)
    X2 = X2.astype(np.float64)
    X3 = np.concatenate((X, X2), axis=0)

    minmaxscale = MinMaxScaler().fit(X3)

    X = minmaxscale.transform(X)
    if isconv:
        X = X.reshape((-1, 1, 28, 28))

    sio.savemat(osp.join(path, 'traindata.mat'), {'X': X, 'Y': Y})

    X2 = minmaxscale.transform(X2)
    if isconv:
        X2 = X2.reshape((-1, 1, 28, 28))

    sio.savemat(osp.join(path, 'testdata.mat'), {'X': X2, 'Y': Y2}) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def doLaplacianSolveWithConstraints(self, evt):
        anchorWeights = 1e8
        anchors = np.zeros((len(self.laplacianConstraints), 3))
        i = 0
        anchorsIdx = []
        for anchor in self.laplacianConstraints:
            anchorsIdx.append(anchor)
            anchors[i, :] = self.laplacianConstraints[anchor]
            i += 1
        
        #IGL Cotangent weights
        (L, M_inv, solver, deltaCoords) = makeLaplacianMatrixSolverIGLSoft(self.mesh.VPos, self.mesh.ITris, anchorsIdx, anchorWeights)
        self.mesh.VPos = solveLaplacianMatrixIGLSoft(solver, L, M_inv, deltaCoords, anchorsIdx, anchors, anchorWeights)
        
#        #My umbrella weights
#        L = makeLaplacianMatrixUmbrellaWeights(self.mesh.VPos, self.mesh.ITris, anchorsIdx, anchorWeights)
#        deltaCoords = L.dot(self.mesh.VPos)[0:self.mesh.VPos.shape[0], :]
#        self.mesh.VPos = np.array(solveLaplacianMatrix(L, deltaCoords, anchors, anchorWeights), dtype=np.float32)
        
        sio.savemat("anchors.mat", {'deltaCoords':deltaCoords, 'anchors':anchors, 'anchorsIdx':np.array(anchorsIdx)})
        self.mesh.needsDisplayUpdate = True
        self.mesh.updateIndexDisplayList()
        self.Refresh() 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def cal_pca_matrix(path='PCA_matrix.mat', ksize=15, l_max=12.0, dim_pca=15, num_samples=500):
    kernels = np.zeros([ksize*ksize, num_samples], dtype=np.float32)
    for i in range(num_samples):

        theta = np.pi*np.random.rand(1)
        l1    = 0.1+l_max*np.random.rand(1)
        l2    = 0.1+(l1-0.1)*np.random.rand(1)

        k = anisotropic_Gaussian(ksize=ksize, theta=theta[0], l1=l1[0], l2=l2[0])

        # util.imshow(k)

        kernels[:, i] = np.reshape(k, (-1), order="F")  # k.flatten(order='F')

    # io.savemat('k.mat', {'k': kernels})

    pca_matrix = get_pca_matrix(kernels, dim_pca=dim_pca)

    io.savemat(path, {'p': pca_matrix})

    return pca_matrix 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def precision_recall(params):
    database_code = np.array(params['database_code'])
    validation_code = np.array(params['validation_code'])
    database_labels = np.array(params['database_labels'])
    validation_labels = np.array(params['validation_labels'])
    database_code = np.sign(database_code)
    validation_code = np.sign(validation_code)
    database_labels.astype(np.int)
    validation_labels.astype(np.int)

    sim = np.dot(database_code, validation_code.T)
    ids = np.argsort(-sim, axis=0)
    ones = np.ones((ids.shape[0], ids.shape[1]), dtype=np.int)
    print(np.min(ids))
    ids = ids + ones
    print(np.min(ids))
    mat_ids = dict(
        ids=ids,
        LBase=database_labels,
        LTest=validation_labels
    )
    scio.savemat('./data/data.mat', mat_ids) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def bench_run():
    str_io = BytesIO()
    print()
    print('Read / writing matlab structs')
    print('='*60)
    print(' write |  read |   vars | fields | structs | compressed')
    print('-'*60)
    print()
    for n_vars, n_fields, n_structs in (
        (10, 10, 20), (20, 20, 40), (30, 30, 50)):
        var_dict = make_structarr(n_vars, n_fields, n_structs)
        for compression in (False, True):
            str_io = BytesIO()
            write_time = measure('sio.savemat(str_io, var_dict, do_compression=%r)' % compression)
            read_time = measure('sio.loadmat(str_io)')
            print('%.5f | %.5f | %5d | %5d | %5d | %r' % (
                write_time,
                read_time,
                n_vars,
                n_fields,
                n_structs,
                compression)) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def createAccount(template, mask, name, exinfo):
	'''
	Description:
		Create an account in database based on extracted feature, and some
		extra information from the enroller.

	Input:
		template 	- Extracted template from the iris image
		mask		- Extracted mask from the iris image
		name		- Name of the enroller
		exinfo		- Extra information of the enroller
	'''
	# Get file name for the account
	files = []
	for file in os.listdir(temp_database_path):
	    if file.endswith(".mat"):
	        files.append(file)
	filename = str(len(files) + 1)

	# Save the file
	sio.savemat(temp_database_path + filename + '.mat',	\
		mdict={'template':template, 'mask':mask,\
		'name':name, 'exinfo':exinfo}) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def predict_probs(img, net, FLAGS, DATA):
  # open image
  cvim = cv2.imread(img, cv2.IMREAD_UNCHANGED)
  if cvim is None:
    print("No image to open for ", img)
    return
  # predict mask from image
  start = time.time()
  probs = net.predict(cvim, path=FLAGS.path + '/' +
                      FLAGS.model, verbose=FLAGS.verbose, as_probs=True)
  print("Prediction for img ", img, ". Elapsed: ", time.time() - start, "s")

  # save to matlab matrix
  matname = FLAGS.log + "/" + \
      os.path.splitext(os.path.basename(img))[0] + ".mat"
  sio.savemat(matname, {'p': probs})

  return 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def stitchPatch(root_folder, dir1, imgname, featfolder, savefolder):  
    # stitch the features of patches to feature of full image
	name = os.path.join(dir1, imgname)
	print 'name:%s\n' %(name)
	Im = os.path.join(featfolder, name[0:-4])
	I = [None]*16
	for i in range(9):
		dict1 = sio.loadmat(Im+'_0'+str(i+1)+'.mat')
		I[i] = dict1['feat']
	for i in range(9,16):
		dict2 = sio.loadmat(Im+'_'+str(i+1)+'.mat')
		I[i] = dict2['feat']
	A = np.zeros((4*500,4*500))
	for row in range(4):
		for col in range(4):
			A[row*500:(row+1)*500,col*500:(col+1)*500] = I[row*4+col]
	sio.savemat(savefolder+name[0:-4], {'A':np.mat(A)}) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def get_feature():
    inputs = tf.placeholder("float", [None, 64, 64, 1])
    is_training = tf.placeholder("bool")
    _, feature = googlenet(inputs, is_training)
    feature = tf.squeeze(feature, [1, 2])
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    saver = tf.train.Saver()
    data = sio.loadmat("../data/dataset.mat")
    testdata = data["test"] / 127.5 - 1.0
    testlabels = data["testlabels"]
    saver.restore(sess, "../save_para/.\\model.ckpt")
    nums_test = testdata.shape[0]
    FEATURE = np.zeros([nums_test, 1024])
    for i in range(nums_test // BATCH_SIZE):
        FEATURE[i * BATCH_SIZE:i * BATCH_SIZE + BATCH_SIZE] = sess.run(feature, feed_dict={inputs: testdata[i * BATCH_SIZE:i * BATCH_SIZE + BATCH_SIZE], is_training: False})
    FEATURE[(nums_test // BATCH_SIZE - 1) * BATCH_SIZE + BATCH_SIZE:] = sess.run(feature, feed_dict={inputs: testdata[(nums_test // BATCH_SIZE - 1) * BATCH_SIZE + BATCH_SIZE:], is_training: False})
    sio.savemat("../data/feature.mat", {"feature": FEATURE, "testlabels": testlabels}) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def tsne():
    data = sio.loadmat("../data/feature.mat")
    feature_test = data["feature"]
    proj = TSNE().fit_transform(feature_test)
    sio.savemat("../data/proj.mat", {"proj": proj}) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def to_file_map(self, file_map=None):
        ''' Write image to `file_map` or contained ``self.file_map``

        Extends Analyze ``to_file_map`` method by writing ``mat`` file

        Parameters
        ----------
        file_map : None or mapping, optional
           files mapping.  If None (default) use object's ``file_map``
           attribute instead
        '''
        if file_map is None:
            file_map = self.file_map
        super(Spm99AnalyzeImage, self).to_file_map(file_map)
        mat = self._affine
        if mat is None:
            return
        import scipy.io as sio
        hdr = self._header
        if hdr.default_x_flip:
            M = np.dot(np.diag([-1, 1, 1, 1]), mat)
        else:
            M = mat
        # Adjust for matlab 1,1,1 voxel origin
        from_111 = np.eye(4)
        from_111[:3,3] = -1
        M = np.dot(M, from_111)
        mat = np.dot(mat, from_111)
        # use matlab 4 format to allow gzipped write without error
        mfobj = file_map['mat'].get_prepare_fileobj(mode='wb')
        sio.savemat(mfobj, {'M': M, 'mat': mat}, format='4')
        if file_map['mat'].filename is not None: # was filename
            mfobj.close() 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def create_random_data(ntrain=10**4, nbase=10**4, nquery=10**2):
    """
    Create random data
    """
    # synthetic dataset
    vtrain, vbase, vquery, ids_gnd = load_random(ntrain, nbase, nquery)
    spio.savemat('./test-tmp/hdidx_test_vbase.mat', {'feat': vbase[:10, :]})

    return np.require(vtrain, np.single, requirements="C"),\
        np.require(vbase, np.single, requirements="C"),    \
        np.require(vquery, np.single, requirements="C"),   \
        ids_gnd 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def save_misc_data(path, X, Y, N):
    threshold_index = int(N * 4/5)
    sio.savemat(osp.join(path, 'traindata.mat'), {'X': X[:threshold_index], 'Y': Y[:threshold_index]})
    sio.savemat(osp.join(path, 'testdata.mat'), {'X': X[threshold_index:], 'Y': Y[threshold_index:]}) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def compressed_data(dataset, n_samples, k, preprocess=None, algo='mknn', isPCA=None, format='mat'):
    datadir = get_data_dir(dataset)
    if format == 'pkl':
        labels, features = load_train_and_validation(load_data, datadir, n_samples)
    elif format == 'h5':
        labels, features = load_train_and_validation(load_data_h5py, datadir, n_samples)
    else:
        labels, features = load_train_and_validation(load_matdata, datadir, n_samples)

    features = feature_transformation(features, preprocessing=preprocess)

    # PCA is computed for Text dataset. Please refer RCC paper for exact details.
    features1 = features.copy()
    if isPCA is not None:
        pca = PCA(n_components=isPCA, svd_solver='full').fit(features)
        features1 = pca.transform(features)

    t0 = time()

    if algo == 'knn':
        weights = kNN(features1, k=k, measure='euclidean')
    else:
        weights = mkNN(features1, k=k, measure='cosine')

    print('The time taken for edge set computation is {}'.format(time() - t0))

    filepath = os.path.join(datadir, 'pretrained')
    if format == 'h5':
        import h5py
        fo = h5py.File(filepath + '.h5', 'w')
        fo.create_dataset('X', data=features)
        fo.create_dataset('w', data=weights[:, :2])
        fo.create_dataset('gtlabels', data=labels)
        fo.close()
    else:
        sio.savemat(filepath + '.mat', mdict={'X': features, 'w': weights[:, :2], 'gtlabels': labels}) 

# 需要導入模塊: from scipy import io [as 別名]
# 或者: from scipy.io import savemat [as 別名]
def SavePredictionScores(pred_scores, adv_scores, im_height, im_width, args, is_debug=False):
    """Saves the outputs of the network in a mat file."""

    pred_scores = softmax(pred_scores)
    adv_scores  = softmax(adv_scores)

    conf = pred_scores.max(axis = 0)
    adv_conf = adv_scores.max(axis = 0)

    entropy_map = entropy(pred_scores)
    conf_ratio_map = conf_ratio(pred_scores)

    adv_entropy_map = entropy(adv_scores)
    adv_conf_ratio_map = conf_ratio(adv_scores)

    model_name = args.model_name
    image_name = os.path.basename(args.image).split('.')[0]
    save_name = os.path.join(
        args.out_dir, "{}_scores_{}_eps={}.mat".format(image_name, model_name, args.eps))

    if not is_debug:
        sio.savemat(save_name, {'conf': conf, 'adv_conf': adv_conf, 'im_height' : im_height, 'im_width': im_width, 'entropy': entropy_map, 'conf_ratio': conf_ratio_map, 'adv_entropy': adv_entropy_map, 'adv_conf_ratio': adv_conf_ratio_map}, do_compression=True)
    else:
        sio.savemat(save_name, {'unary': pred_scores, 'unary_adv': adv_scores, 'conf': conf, 'adv_conf': adv_conf, 'im_height' : im_height, 'im_width': im_width, 'entropy': entropy_map, 'conf_ratio': conf_ratio_map, 'adv_entropy': adv_entropy_map, 'adv_conf_ratio': adv_conf_ratio_map}, do_compression=True)
    
    return conf