Python numpy.zeros() Examples
The following are 30 code examples for showing how to use numpy.zeros(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: Financial-NLP Author: Coldog2333 File: NLP.py License: Apache License 2.0 | 7 votes |
def wordbag2mat(self, wordbag): #testing if self.model==None: raise Exception("no model") matrix=np.empty((len(wordbag),self.len_vector)) #如果词典中不存在该词,抛出异常,但暂时还没有自定义词典的办法,所以暂时不那么严格 #try: # for i in range(len(wordbag)): # matrix[i,:]=self.model[wordbag[i]] #except: # raise Exception("'%s' can not be found in dictionary." % wordbag[i]) #如果词典中不存在该词,则push进一列零向量 for i in range(len(wordbag)): try: matrix[i,:]=self.model.wv.__getitem__(wordbag[i])#[wordbag[i]] except: matrix[i,:]=np.zeros((1,self.len_vector)) return matrix ################################ problem #####################################
Example 2
Project: Caffe-Python-Data-Layer Author: liuxianming File: MultiLabelLayer.py License: BSD 2-Clause "Simplified" License | 6 votes |
def get_a_datum(self): if self._compressed: datum = extract_sample( self._data[self._cur], self._mean, self._resize) else: datum = self._data[self._cur] # start parsing labels label_elems = parse_label(self._label[self._cur]) label = np.zeros(self._label_dim) if not self._multilabel: label[0] = label_elems[0] else: for i in label_elems: label[i] = 1 self._cur = (self._cur + 1) % self._sample_count return datum, label
Example 3
Project: Financial-NLP Author: Coldog2333 File: NLP.py License: Apache License 2.0 | 6 votes |
def similarity_label(self, words, normalization=True): """ you can calculate more than one word at the same time. """ if self.model==None: raise Exception('no model.') if isinstance(words, string_types): words=[words] vectors=np.transpose(self.model.wv.__getitem__(words)) if normalization: unit_vector=unitvec(vectors,ax=0) # 这样写比原来那样速度提升一倍 #unit_vector=np.zeros((len(vectors),len(words))) #for i in range(len(words)): # unit_vector[:,i]=matutils.unitvec(vectors[:,i]) dists=np.dot(self.Label_vec_u, unit_vector) else: dists=np.dot(self.Label_vec, vectors) return dists
Example 4
Project: cat-bbs Author: aleju File: create_dataset.py License: MIT License | 6 votes |
def load_keypoints(image_filepath, image_height, image_width): """Load facial keypoints of one image.""" fp_keypoints = "%s.cat" % (image_filepath,) if not os.path.isfile(fp_keypoints): raise Exception("Could not find keypoint coordinates for image '%s'." \ % (image_filepath,)) else: coords_raw = open(fp_keypoints, "r").readlines()[0].strip().split(" ") coords_raw = [abs(int(coord)) for coord in coords_raw] keypoints = [] #keypoints_arr = np.zeros((9*2,), dtype=np.int32) for i in range(1, len(coords_raw), 2): # first element is the number of coords x = np.clip(coords_raw[i], 0, image_width-1) y = np.clip(coords_raw[i+1], 0, image_height-1) keypoints.append((x, y)) return keypoints
Example 5
Project: LipNet-PyTorch Author: sailordiary File: ctc_decoder.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def wer(self, r, h): # initialisation d = np.zeros((len(r)+1)*(len(h)+1), dtype=np.uint8) d = d.reshape((len(r)+1, len(h)+1)) for i in range(len(r)+1): for j in range(len(h)+1): if i == 0: d[0][j] = j elif j == 0: d[i][0] = i # computation for i in range(1, len(r)+1): for j in range(1, len(h)+1): if r[i-1] == h[j-1]: d[i][j] = d[i-1][j-1] else: substitution = d[i-1][j-1] + 1 insertion = d[i][j-1] + 1 deletion = d[i-1][j] + 1 d[i][j] = min(substitution, insertion, deletion) return d[len(r)][len(h)]
Example 6
Project: fenics-topopt Author: zfergus File: solver.py License: MIT License | 6 votes |
def compliance_function_fdiff(self, x, dc): obj = self.compliance_function(x, dc) x0 = x.copy() dc0 = dc.copy() dcf = np.zeros(dc.shape) for i, v in enumerate(x): x = x0.copy() x[i] += 1e-6 o1 = self.compliance_function(x, dc) x[i] = x0[i] - 1e-6 o2 = self.compliance_function(x, dc) dcf[i] = (o1 - o2) / (2e-6) print("finite differences: {:g}".format(np.linalg.norm(dcf - dc0))) dc[:] = dc0 return obj
Example 7
Project: fenics-topopt Author: zfergus File: von_mises_stress.py License: MIT License | 6 votes |
def calculate_fdiff_stress(self, x, u, nu, side=1, dx=1e-6): """ Calculate the derivative of the Von Mises stress using finite differences given the densities x, displacements u, and young modulus nu. Optionally, provide the side length (default: 1) and delta x (default: 1e-6). """ ds = self.calculate_diff_stress(x, u, nu, side) dsf = numpy.zeros(x.shape) x = numpy.expand_dims(x, -1) for i in range(x.shape[0]): delta = scipy.sparse.coo_matrix(([dx], [[i], [0]]), shape=x.shape) s1 = self.calculate_stress((x + delta.A).squeeze(), u, nu, side) s2 = self.calculate_stress((x - delta.A).squeeze(), u, nu, side) dsf[i] = ((s1 - s2) / (2. * dx))[i] print("finite differences: {:g}".format(numpy.linalg.norm(dsf - ds))) return dsf
Example 8
Project: fenics-topopt Author: zfergus File: solver.py License: MIT License | 6 votes |
def compliance_function_fdiff(self, x, dc): obj = self.compliance_function(x, dc) x0 = x.copy() dc0 = dc.copy() dcf = np.zeros(dc.shape) for i, v in enumerate(x): x = x0.copy() x[i] += 1e-6 o1 = self.compliance_function(x, dc) x[i] = x0[i] - 1e-6 o2 = self.compliance_function(x, dc) dcf[i] = (o1 - o2) / (2e-6) print("finite differences: {:g}".format(np.linalg.norm(dcf - dc0))) dc[:] = dc0 return obj
Example 9
Project: fenics-topopt Author: zfergus File: von_mises_stress.py License: MIT License | 6 votes |
def calculate_fdiff_stress(self, x, u, nu, side=1, dx=1e-6): """ Calculate the derivative of the Von Mises stress using finite differences given the densities x, displacements u, and young modulus nu. Optionally, provide the side length (default: 1) and delta x (default: 1e-6). """ ds = self.calculate_diff_stress(x, u, nu, side) dsf = numpy.zeros(x.shape) x = numpy.expand_dims(x, -1) for i in range(x.shape[0]): delta = scipy.sparse.coo_matrix(([dx], [[i], [0]]), shape=x.shape) s1 = self.calculate_stress((x + delta.A).squeeze(), u, nu, side) s2 = self.calculate_stress((x - delta.A).squeeze(), u, nu, side) dsf[i] = ((s1 - s2) / (2. * dx))[i] print("finite differences: {:g}".format(numpy.linalg.norm(dsf - ds))) return dsf
Example 10
Project: aospy Author: spencerahill File: test_utils_times.py License: Apache License 2.0 | 6 votes |
def test_add_uniform_time_weights(): time = np.array([15, 46, 74]) data = np.zeros((3)) ds = xr.DataArray(data, coords=[time], dims=[TIME_STR], name='a').to_dataset() units_str = 'days since 2000-01-01 00:00:00' cal_str = 'noleap' ds[TIME_STR].attrs['units'] = units_str ds[TIME_STR].attrs['calendar'] = cal_str with pytest.raises(KeyError): ds[TIME_WEIGHTS_STR] ds = add_uniform_time_weights(ds) time_weights_expected = xr.DataArray( [1, 1, 1], coords=ds[TIME_STR].coords, name=TIME_WEIGHTS_STR) time_weights_expected.attrs['units'] = 'days' assert ds[TIME_WEIGHTS_STR].identical(time_weights_expected)
Example 11
Project: aospy Author: spencerahill File: conftest.py License: Apache License 2.0 | 6 votes |
def ds_time_encoded_cf(): time_bounds = np.array([[0, 31], [31, 59], [59, 90]]) bounds = np.array([0, 1]) time = np.array([15, 46, 74]) data = np.zeros((3)) ds = xr.DataArray(data, coords=[time], dims=[TIME_STR], name='a').to_dataset() ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds, coords=[time, bounds], dims=[TIME_STR, BOUNDS_STR], name=TIME_BOUNDS_STR) units_str = 'days since 2000-01-01 00:00:00' cal_str = 'noleap' ds[TIME_STR].attrs['units'] = units_str ds[TIME_STR].attrs['calendar'] = cal_str return ds
Example 12
Project: aospy Author: spencerahill File: test_data_loader.py License: Apache License 2.0 | 6 votes |
def ds_with_time_bounds(alt_lat_str, var_name): time_bounds = np.array([[0, 31], [31, 59], [59, 90]]) bounds = np.array([0, 1]) time = np.array([15, 46, 74]) data = np.zeros((3, 1, 1)) lat = [0] lon = [0] ds = xr.DataArray(data, coords=[time, lat, lon], dims=[TIME_STR, alt_lat_str, LON_STR], name=var_name).to_dataset() ds[TIME_BOUNDS_STR] = xr.DataArray(time_bounds, coords=[time, bounds], dims=[TIME_STR, BOUNDS_STR], name=TIME_BOUNDS_STR) units_str = 'days since 2000-01-01 00:00:00' ds[TIME_STR].attrs['units'] = units_str ds[TIME_BOUNDS_STR].attrs['units'] = units_str return ds
Example 13
Project: aospy Author: spencerahill File: test_data_loader.py License: Apache License 2.0 | 6 votes |
def test_sel_var(): time = np.array([0, 31, 59]) + 15 data = np.zeros((3)) ds = xr.DataArray(data, coords=[time], dims=[TIME_STR], name=convection_rain.name).to_dataset() condensation_rain_alt_name, = condensation_rain.alt_names ds[condensation_rain_alt_name] = xr.DataArray(data, coords=[ds.time]) result = _sel_var(ds, convection_rain) assert result.name == convection_rain.name result = _sel_var(ds, condensation_rain) assert result.name == condensation_rain.name with pytest.raises(LookupError): _sel_var(ds, precip)
Example 14
Project: Att-ChemdNER Author: lingluodlut File: nn.py License: Apache License 2.0 | 6 votes |
def build(self): #{{{ import numpy as np; self.W = shared((self.input_dim, 4 * self.output_dim), name='{}_W'.format(self.name)) self.U = shared((self.output_dim, 4 * self.output_dim), name='{}_U'.format(self.name)) self.b = K.variable(np.hstack((np.zeros(self.output_dim), K.get_value(self.forget_bias_init( (self.output_dim,))), np.zeros(self.output_dim), np.zeros(self.output_dim))), name='{}_b'.format(self.name)) #self.c_0 = shared((self.output_dim,), name='{}_c_0'.format(self.name) ) #self.h_0 = shared((self.output_dim,), name='{}_h_0'.format(self.name) ) self.c_0=np.zeros(self.output_dim).astype(theano.config.floatX); self.h_0=np.zeros(self.output_dim).astype(theano.config.floatX); self.params=[self.W,self.U, self.b, # self.c_0,self.h_0 ]; #}}}
Example 15
Project: Att-ChemdNER Author: lingluodlut File: theano_backend.py License: Apache License 2.0 | 6 votes |
def ctc_update_log_p(skip_idxs, zeros, active, log_p_curr, log_p_prev): active_skip_idxs = skip_idxs[(skip_idxs < active).nonzero()] active_next = T.cast(T.minimum( T.maximum( active + 1, T.max(T.concatenate([active_skip_idxs, [-1]])) + 2 + 1 ), log_p_curr.shape[0]), 'int32') common_factor = T.max(log_p_prev[:active]) p_prev = T.exp(log_p_prev[:active] - common_factor) _p_prev = zeros[:active_next] # copy over _p_prev = T.set_subtensor(_p_prev[:active], p_prev) # previous transitions _p_prev = T.inc_subtensor(_p_prev[1:], _p_prev[:-1]) # skip transitions _p_prev = T.inc_subtensor(_p_prev[active_skip_idxs + 2], p_prev[active_skip_idxs]) updated_log_p_prev = T.log(_p_prev) + common_factor log_p_next = T.set_subtensor( zeros[:active_next], log_p_curr[:active_next] + updated_log_p_prev ) return active_next, log_p_next
Example 16
Project: Att-ChemdNER Author: lingluodlut File: theano_backend.py License: Apache License 2.0 | 6 votes |
def ctc_path_probs(predict, Y, alpha=1e-4): smoothed_predict = (1 - alpha) * predict[:, Y] + alpha * np.float32(1.) / Y.shape[0] L = T.log(smoothed_predict) zeros = T.zeros_like(L[0]) log_first = zeros f_skip_idxs = ctc_create_skip_idxs(Y) b_skip_idxs = ctc_create_skip_idxs(Y[::-1]) # there should be a shortcut to calculating this def step(log_f_curr, log_b_curr, f_active, log_f_prev, b_active, log_b_prev): f_active_next, log_f_next = ctc_update_log_p(f_skip_idxs, zeros, f_active, log_f_curr, log_f_prev) b_active_next, log_b_next = ctc_update_log_p(b_skip_idxs, zeros, b_active, log_b_curr, log_b_prev) return f_active_next, log_f_next, b_active_next, log_b_next [f_active, log_f_probs, b_active, log_b_probs], _ = theano.scan( step, sequences=[L, L[::-1, ::-1]], outputs_info=[np.int32(1), log_first, np.int32(1), log_first]) idxs = T.arange(L.shape[1]).dimshuffle('x', 0) mask = (idxs < f_active.dimshuffle(0, 'x')) & (idxs < b_active.dimshuffle(0, 'x'))[::-1, ::-1] log_probs = log_f_probs + log_b_probs[::-1, ::-1] - L return log_probs, mask
Example 17
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: test.py License: MIT License | 6 votes |
def _project_im_rois(im_rois, scales): """Project image RoIs into the image pyramid built by _get_image_blob. Arguments: im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates scales (list): scale factors as returned by _get_image_blob Returns: rois (ndarray): R x 4 matrix of projected RoI coordinates levels (list): image pyramid levels used by each projected RoI """ im_rois = im_rois.astype(np.float, copy=False) if len(scales) > 1: widths = im_rois[:, 2] - im_rois[:, 0] + 1 heights = im_rois[:, 3] - im_rois[:, 1] + 1 areas = widths * heights scaled_areas = areas[:, np.newaxis] * (scales[np.newaxis, :] ** 2) diff_areas = np.abs(scaled_areas - 224 * 224) levels = diff_areas.argmin(axis=1)[:, np.newaxis] else: levels = np.zeros((im_rois.shape[0], 1), dtype=np.int) rois = im_rois * scales[levels] return rois, levels
Example 18
Project: unicorn-hat-hd Author: pimoroni File: __init__.py License: MIT License | 5 votes |
def setup_buffer(width, height): """Set up the internal pixel buffer. :param width: width of buffer, ideally in multiples of 16 :param height: height of buffer, ideally in multiples of 16 """ global _buffer_width, _buffer_height, _buf _buffer_width = width _buffer_height = height _buf = numpy.zeros((_buffer_width, _buffer_height, 3), dtype=int)
Example 19
Project: Black-Box-Audio Author: rtaori File: tf_logits.py License: MIT License | 5 votes |
def compute_mfcc(audio, **kwargs): """ Compute the MFCC for a given audio waveform. This is identical to how DeepSpeech does it, but does it all in TensorFlow so that we can differentiate through it. """ batch_size, size = audio.get_shape().as_list() audio = tf.cast(audio, tf.float32) # 1. Pre-emphasizer, a high-pass filter audio = tf.concat((audio[:, :1], audio[:, 1:] - 0.97*audio[:, :-1], np.zeros((batch_size,1000),dtype=np.float32)), 1) # 2. windowing into frames of 320 samples, overlapping windowed = tf.stack([audio[:, i:i+400] for i in range(0,size-320,160)],1) # 3. Take the FFT to convert to frequency space ffted = tf.spectral.rfft(windowed, [512]) ffted = 1.0 / 512 * tf.square(tf.abs(ffted)) # 4. Compute the Mel windowing of the FFT energy = tf.reduce_sum(ffted,axis=2)+1e-30 filters = np.load("filterbanks.npy").T feat = tf.matmul(ffted, np.array([filters]*batch_size,dtype=np.float32))+1e-30 # 5. Take the DCT again, because why not feat = tf.log(feat) feat = tf.spectral.dct(feat, type=2, norm='ortho')[:,:,:26] # 6. Amplify high frequencies for some reason _,nframes,ncoeff = feat.get_shape().as_list() n = np.arange(ncoeff) lift = 1 + (22/2.)*np.sin(np.pi*n/22) feat = lift*feat width = feat.get_shape().as_list()[1] # 7. And now stick the energy next to the features feat = tf.concat((tf.reshape(tf.log(energy),(-1,width,1)), feat[:, :, 1:]), axis=2) return feat
Example 20
Project: Black-Box-Audio Author: rtaori File: tf_logits.py License: MIT License | 5 votes |
def get_logits(new_input, length, first=[]): """ Compute the logits for a given waveform. First, preprocess with the TF version of MFC above, and then call DeepSpeech on the features. """ # new_input = tf.Print(new_input, [tf.shape(new_input)]) # We need to init DeepSpeech the first time we're called if first == []: first.append(False) # Okay, so this is ugly again. # We just want it to not crash. tf.app.flags.FLAGS.alphabet_config_path = "DeepSpeech/data/alphabet.txt" DeepSpeech.initialize_globals() print('initialized deepspeech globals') batch_size = new_input.get_shape()[0] # 1. Compute the MFCCs for the input audio # (this is differentable with our implementation above) empty_context = np.zeros((batch_size, 9, 26), dtype=np.float32) new_input_to_mfcc = compute_mfcc(new_input)[:, ::2] features = tf.concat((empty_context, new_input_to_mfcc, empty_context), 1) # 2. We get to see 9 frames at a time to make our decision, # so concatenate them together. features = tf.reshape(features, [new_input.get_shape()[0], -1]) features = tf.stack([features[:, i:i+19*26] for i in range(0,features.shape[1]-19*26+1,26)],1) features = tf.reshape(features, [batch_size, -1, 19*26]) # 3. Whiten the data mean, var = tf.nn.moments(features, axes=[0,1,2]) features = (features-mean)/(var**.5) # 4. Finally we process it with DeepSpeech logits = DeepSpeech.BiRNN(features, length, [0]*10) return logits
Example 21
Project: svviz Author: svviz File: kde.py License: MIT License | 5 votes |
def evaluate(self, points): points = atleast_2d(points) d, m = points.shape if d != self.d: if d == 1 and m == self.d: # points was passed in as a row vector points = reshape(points, (self.d, 1)) m = 1 else: msg = "points have dimension %s, dataset has dimension %s" % (d, self.d) raise ValueError(msg) result = zeros((m,), dtype=np.float) if m >= self.n: # there are more points than data, so loop over data for i in range(self.n): diff = self.dataset[:, i, newaxis] - points tdiff = dot(self.inv_cov, diff) energy = sum(diff*tdiff,axis=0) / 2.0 result = result + exp(-energy) else: # loop over points for i in range(m): diff = self.dataset - points[:, i, newaxis] tdiff = dot(self.inv_cov, diff) energy = sum(diff * tdiff, axis=0) / 2.0 result[i] = sum(exp(-energy), axis=0) result = result / self._norm_factor return result
Example 22
Project: libTLDA Author: wmkouw File: util.py License: MIT License | 5 votes |
def one_hot(y, fill_k=False, one_not=False): """Map to one-hot encoding.""" # Check labels labels = np.unique(y) # Number of classes K = len(labels) # Number of samples N = y.shape[0] # Preallocate array if one_not: Y = -np.ones((N, K)) else: Y = np.zeros((N, K)) # Set k-th column to 1 for n-th sample for n in range(N): # Map current class to index label y_n = (y[n] == labels) if fill_k: Y[n, y_n] = y_n else: Y[n, y_n] = 1 return Y, labels
Example 23
Project: libTLDA Author: wmkouw File: rba.py License: MIT License | 5 votes |
def psi(self, X, theta, w, K=2): """ Compute psi function. Parameters ---------- X : array data set (N samples by D features) theta : array classifier parameters (D features by 1) w : array importance-weights (N samples by 1) K : int number of classes (def: 2) Returns ------- psi : array array with psi function values (N samples by K classes) """ # Number of samples N = X.shape[0] # Preallocate psi array psi = np.zeros((N, K)) # Loop over classes for k in range(K): # Compute feature statistics Xk = self.feature_stats(X, k*np.ones((N, 1))) # Compute psi function psi[:, k] = (w*np.dot(Xk, theta))[:, 0] return psi
Example 24
Project: libTLDA Author: wmkouw File: rba.py License: MIT License | 5 votes |
def posterior(self, psi): """ Class-posterior estimation. Parameters ---------- psi : array weighted data-classifier output (N samples by K classes) Returns ------- pyx : array class-posterior estimation (N samples by K classes) """ # Data shape N, K = psi.shape # Preallocate array pyx = np.zeros((N, K)) # Subtract maximum value for numerical stability psi = (psi.T - np.max(psi, axis=1).T).T # Loop over classes for k in range(K): # Estimate posterior p^(Y=y | x_i) pyx[:, k] = np.exp(psi[:, k]) / np.sum(np.exp(psi), axis=1) return pyx
Example 25
Project: libTLDA Author: wmkouw File: test_suba.py License: MIT License | 5 votes |
def test_fit_semi(): """Test for fitting the model.""" X = rnd.randn(10, 2) y = np.hstack((np.zeros((5,)), np.ones((5,)))) Z = rnd.randn(10, 2) + 1 u = np.array([[0, 0], [9, 1]]) clf = SemiSubspaceAlignedClassifier() clf.fit(X, y, Z, u) assert clf.is_trained
Example 26
Project: libTLDA Author: wmkouw File: test_suba.py License: MIT License | 5 votes |
def test_predict_semi(): """Test for making predictions.""" X = rnd.randn(10, 2) y = np.hstack((np.zeros((5,)), np.ones((5,)))) Z = rnd.randn(10, 2) + 1 u = np.array([[0, 0], [9, 1]]) clf = SemiSubspaceAlignedClassifier() clf.fit(X, y, Z, u) u_pred = clf.predict(Z) labels = np.unique(y) assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0
Example 27
Project: libTLDA Author: wmkouw File: test_rba.py License: MIT License | 5 votes |
def test_fit(): """Test for fitting the model.""" X = rnd.randn(10, 2) y = np.hstack((np.zeros((5,)), np.ones((5,)))) Z = rnd.randn(10, 2) + 1 clf = RobustBiasAwareClassifier() clf.fit(X, y, Z) assert clf.is_trained
Example 28
Project: libTLDA Author: wmkouw File: test_rba.py License: MIT License | 5 votes |
def test_predict(): """Test for making predictions.""" X = rnd.randn(10, 2) y = np.hstack((np.zeros((5,)), np.ones((5,)))) Z = rnd.randn(10, 2) + 1 clf = RobustBiasAwareClassifier() clf.fit(X, y, Z) u_pred = clf.predict(Z) labels = np.unique(y) assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0
Example 29
Project: libTLDA Author: wmkouw File: test_tcpr.py License: MIT License | 5 votes |
def test_fit(): """Test for fitting the model.""" X = np.vstack((rnd.randn(5, 2), rnd.randn(5, 2)+1)) y = np.hstack((np.zeros((5,)), np.ones((5,)))) Z = np.vstack((rnd.randn(5, 2)-1, rnd.randn(5, 2)+2)) clf = TargetContrastivePessimisticClassifier(l2=0.1) clf.fit(X, y, Z) assert clf.is_trained
Example 30
Project: libTLDA Author: wmkouw File: test_scl.py License: MIT License | 5 votes |
def test_init(): """Test for object type.""" clf = StructuralCorrespondenceClassifier() assert type(clf) == StructuralCorrespondenceClassifier assert not clf.is_trained # def test_fit(): # """Test for fitting the model.""" # X = np.vstack((rnd.randn(5, 2), rnd.randn(5, 2)+1)) # y = np.hstack((np.zeros((5,)), np.ones((5,)))) # Z = np.vstack((rnd.randn(5, 2)-1, rnd.randn(5, 2)+2)) # clf = StructuralCorrespondenceClassifier(l2=1.0) # clf.fit(X, y, Z) # assert clf.is_trained # def test_predict(): # """Test for making predictions.""" # X = np.vstack((rnd.randn(5, 2), rnd.randn(5, 2)+1)) # y = np.hstack((np.zeros((5,)), np.ones((5,)))) # Z = np.vstack((rnd.randn(5, 2)-1, rnd.randn(5, 2)+2)) # clf = StructuralCorrespondenceClassifier(l2=1.0) # clf.fit(X, y, Z) # u_pred = clf.predict(Z) # labels = np.unique(y) # assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0