我们从Python开源项目中，提取了以下24个代码示例，用于说明如何使用sklearn.linear_model.RANSACRegressor()。
def _vlines(lines, ctrs=None, lengths=None, vecs=None, angle_lo=20, angle_hi=160, ransac_options=RANSAC_OPTIONS): ctrs = ctrs if ctrs is not None else lines.mean(1) vecs = vecs if vecs is not None else lines[:, 1, :] - lines[:, 0, :] lengths = lengths if lengths is not None else np.hypot(vecs[:, 0], vecs[:, 1]) angles = np.degrees(np.arccos(vecs[:, 0] / lengths)) points = np.column_stack([ctrs[:, 0], angles]) point_indices, = np.nonzero((angles > angle_lo) & (angles < angle_hi)) points = points[point_indices] if len(points) > 2: model_ransac = linear_model.RANSACRegressor(**ransac_options) model_ransac.fit(points[:, 0].reshape(-1, 1), points[:, 1].reshape(-1, 1)) inlier_mask = model_ransac.inlier_mask_ valid_lines = lines[point_indices[inlier_mask], :, :] else: valid_lines = [] return valid_lines
def _hlines(lines, ctrs=None, lengths=None, vecs=None, angle_lo=20, angle_hi=160, ransac_options=RANSAC_OPTIONS): ctrs = ctrs if ctrs is not None else lines.mean(1) vecs = vecs if vecs is not None else lines[:, 1, :] - lines[:, 0, :] lengths = lengths if lengths is not None else np.hypot(vecs[:, 0], vecs[:, 1]) angles = np.degrees(np.arccos(vecs[:, 1] / lengths)) points = np.column_stack([ctrs[:, 1], angles]) point_indices, = np.nonzero((angles > angle_lo) & (angles < angle_hi)) points = points[point_indices] if len(points) > 2: model_ransac = linear_model.RANSACRegressor(**ransac_options) model_ransac.fit(points[:, 0].reshape(-1, 1), points[:, 1].reshape(-1, 1)) inlier_mask = model_ransac.inlier_mask_ valid_lines = lines[point_indices[inlier_mask], :, :] else: valid_lines = [] return valid_lines
def __init__(self, ompath, density = 4.0): """ :param ompath: path of the mesh template author: weiwei date: 20170711 """ cadtemp = CADTemp.CADTemp(ompath = ompath, density = density) self.objnp = pg.packpandanp(cadtemp.objtrimesh.vertices, cadtemp.objtrimesh.face_normals, cadtemp.objtrimesh.faces, name='') self.temppnt = cadtemp.pcdtemp self.kinect = PyKinectRuntime.PyKinectRuntime(PyKinectV2.FrameSourceTypes_Depth) self.dbscan = DBSCAN(eps=50, min_samples=100, n_jobs=-1) self.randsac = linear_model.RANSACRegressor(linear_model.LinearRegression(), residual_threshold = 15) self.tablepnt = [] self.objectpnt = []
def test_ransac_is_data_valid(): def is_data_valid(X, y): assert_equal(X.shape[0], 2) assert_equal(y.shape[0], 2) return False X = np.random.rand(10, 2) y = np.random.rand(10, 1) base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, is_data_valid=is_data_valid, random_state=0) assert_raises(ValueError, ransac_estimator.fit, X, y)
def test_ransac_multi_dimensional_targets(): base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0) # 3-D target values yyy = np.column_stack([y, y, y]) # Estimate parameters of corrupted data ransac_estimator.fit(X, yyy) # Ground truth / reference inlier mask ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_ ).astype(np.bool_) ref_inlier_mask[outliers] = False assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask) # XXX: Remove in 0.20
def __init__(self): """ Kinect interface author: weiwei date: 20170715 """ self.kinect = PyKinectRuntime.PyKinectRuntime(PyKinectV2.FrameSourceTypes_Depth) self.dbscan = DBSCAN(eps=50, min_samples=100, n_jobs=-1) self.randsac = linear_model.RANSACRegressor(linear_model.LinearRegression(), residual_threshold = 15)
def test_ransac_inliers_outliers(): base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0) # Estimate parameters of corrupted data ransac_estimator.fit(X, y) # Ground truth / reference inlier mask ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_ ).astype(np.bool_) ref_inlier_mask[outliers] = False assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_is_model_valid(): def is_model_valid(estimator, X, y): assert_equal(X.shape[0], 2) assert_equal(y.shape[0], 2) return False base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, is_model_valid=is_model_valid, random_state=0) assert_raises(ValueError, ransac_estimator.fit, X, y)
def test_ransac_max_trials(): base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, max_trials=0, random_state=0) assert_raises(ValueError, ransac_estimator.fit, X, y) ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, max_trials=11, random_state=0) assert getattr(ransac_estimator, 'n_trials_', None) is None ransac_estimator.fit(X, y) assert_equal(ransac_estimator.n_trials_, 2)
def test_ransac_stop_n_inliers(): base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, stop_n_inliers=2, random_state=0) ransac_estimator.fit(X, y) assert_equal(ransac_estimator.n_trials_, 1)
def test_ransac_score(): X = np.arange(100)[:, None] y = np.zeros((100, )) y[0] = 1 y[1] = 100 base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=0.5, random_state=0) ransac_estimator.fit(X, y) assert_equal(ransac_estimator.score(X[2:], y[2:]), 1) assert_less(ransac_estimator.score(X[:2], y[:2]), 1)
def test_ransac_predict(): X = np.arange(100)[:, None] y = np.zeros((100, )) y[0] = 1 y[1] = 100 base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=0.5, random_state=0) ransac_estimator.fit(X, y) assert_equal(ransac_estimator.predict(X), np.zeros(100))
def test_ransac_resid_thresh_no_inliers(): # When residual_threshold=0.0 there are no inliers and a # ValueError with a message should be raised base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=0.0, random_state=0) assert_raises_regexp(ValueError, "No inliers.*residual_threshold.*0\.0", ransac_estimator.fit, X, y)
def test_ransac_sparse_coo(): X_sparse = sparse.coo_matrix(X) base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0) ransac_estimator.fit(X_sparse, y) ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_ ).astype(np.bool_) ref_inlier_mask[outliers] = False assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_sparse_csr(): X_sparse = sparse.csr_matrix(X) base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0) ransac_estimator.fit(X_sparse, y) ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_ ).astype(np.bool_) ref_inlier_mask[outliers] = False assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_none_estimator(): base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0) ransac_none_estimator = RANSACRegressor(None, 2, 5, random_state=0) ransac_estimator.fit(X, y) ransac_none_estimator.fit(X, y) assert_array_almost_equal(ransac_estimator.predict(X), ransac_none_estimator.predict(X))
def test_ransac_min_n_samples(): base_estimator = LinearRegression() ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0) ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2. / X.shape[0], residual_threshold=5, random_state=0) ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=-1, residual_threshold=5, random_state=0) ransac_estimator4 = RANSACRegressor(base_estimator, min_samples=5.2, residual_threshold=5, random_state=0) ransac_estimator5 = RANSACRegressor(base_estimator, min_samples=2.0, residual_threshold=5, random_state=0) ransac_estimator6 = RANSACRegressor(base_estimator, residual_threshold=5, random_state=0) ransac_estimator7 = RANSACRegressor(base_estimator, min_samples=X.shape[0] + 1, residual_threshold=5, random_state=0) ransac_estimator1.fit(X, y) ransac_estimator2.fit(X, y) ransac_estimator5.fit(X, y) ransac_estimator6.fit(X, y) assert_array_almost_equal(ransac_estimator1.predict(X), ransac_estimator2.predict(X)) assert_array_almost_equal(ransac_estimator1.predict(X), ransac_estimator5.predict(X)) assert_array_almost_equal(ransac_estimator1.predict(X), ransac_estimator6.predict(X)) assert_raises(ValueError, ransac_estimator3.fit, X, y) assert_raises(ValueError, ransac_estimator4.fit, X, y) assert_raises(ValueError, ransac_estimator7.fit, X, y)
def test_ransac_residual_metric(): residual_metric1 = lambda dy: np.sum(np.abs(dy), axis=1) residual_metric2 = lambda dy: np.sum(dy ** 2, axis=1) yyy = np.column_stack([y, y, y]) base_estimator = LinearRegression() ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0) ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0, residual_metric=residual_metric1) ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0, residual_metric=residual_metric2) # multi-dimensional ransac_estimator0.fit(X, yyy) assert_warns(DeprecationWarning, ransac_estimator1.fit, X, yyy) assert_warns(DeprecationWarning, ransac_estimator2.fit, X, yyy) assert_array_almost_equal(ransac_estimator0.predict(X), ransac_estimator1.predict(X)) assert_array_almost_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X)) # one-dimensional ransac_estimator0.fit(X, y) assert_warns(DeprecationWarning, ransac_estimator2.fit, X, y) assert_array_almost_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X))
def test_ransac_residual_loss(): loss_multi1 = lambda y_true, y_pred: np.sum(np.abs(y_true - y_pred), axis=1) loss_multi2 = lambda y_true, y_pred: np.sum((y_true - y_pred) ** 2, axis=1) loss_mono = lambda y_true, y_pred : np.abs(y_true - y_pred) yyy = np.column_stack([y, y, y]) base_estimator = LinearRegression() ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0) ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0, loss=loss_multi1) ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0, loss=loss_multi2) # multi-dimensional ransac_estimator0.fit(X, yyy) ransac_estimator1.fit(X, yyy) ransac_estimator2.fit(X, yyy) assert_array_almost_equal(ransac_estimator0.predict(X), ransac_estimator1.predict(X)) assert_array_almost_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X)) # one-dimensional ransac_estimator0.fit(X, y) ransac_estimator2.loss = loss_mono ransac_estimator2.fit(X, y) assert_array_almost_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X)) ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=2, residual_threshold=5, random_state=0, loss="squared_loss") ransac_estimator3.fit(X, y) assert_array_almost_equal(ransac_estimator0.predict(X), ransac_estimator2.predict(X))
def test_ransac_dynamic_max_trials(): # Numbers hand-calculated and confirmed on page 119 (Table 4.3) in # Hartley, R.~I. and Zisserman, A., 2004, # Multiple View Geometry in Computer Vision, Second Edition, # Cambridge University Press, ISBN: 0521540518 # e = 0%, min_samples = X assert_equal(_dynamic_max_trials(100, 100, 2, 0.99), 1) # e = 5%, min_samples = 2 assert_equal(_dynamic_max_trials(95, 100, 2, 0.99), 2) # e = 10%, min_samples = 2 assert_equal(_dynamic_max_trials(90, 100, 2, 0.99), 3) # e = 30%, min_samples = 2 assert_equal(_dynamic_max_trials(70, 100, 2, 0.99), 7) # e = 50%, min_samples = 2 assert_equal(_dynamic_max_trials(50, 100, 2, 0.99), 17) # e = 5%, min_samples = 8 assert_equal(_dynamic_max_trials(95, 100, 8, 0.99), 5) # e = 10%, min_samples = 8 assert_equal(_dynamic_max_trials(90, 100, 8, 0.99), 9) # e = 30%, min_samples = 8 assert_equal(_dynamic_max_trials(70, 100, 8, 0.99), 78) # e = 50%, min_samples = 8 assert_equal(_dynamic_max_trials(50, 100, 8, 0.99), 1177) # e = 0%, min_samples = 10 assert_equal(_dynamic_max_trials(1, 100, 10, 0), 0) assert_equal(_dynamic_max_trials(1, 100, 10, 1), float('inf')) base_estimator = LinearRegression() ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, stop_probability=-0.1) assert_raises(ValueError, ransac_estimator.fit, X, y) ransac_estimator = RANSACRegressor(base_estimator, min_samples=2, stop_probability=1.1) assert_raises(ValueError, ransac_estimator.fit, X, y)
def slope(f, psd, fslopelim = (80,200), flatten_thresh = 0): ''' Calculate the slope of the power spectrum Parameters ---------- f : array frequencies corresponding to power spectrum psd : array power spectrum fslopelim : 2-element list frequency range to fit slope flatten_thresh : float See foof.utils Returns ------- slope : float slope of psd slopelineP : array linear fit of the PSD in log-log space (includes information of offset) slopelineF : array frequency array corresponding to slopebyf ''' fslopeidx = np.logical_and(f>=fslopelim[0],f<=fslopelim[1]) slopelineF = f[fslopeidx] x = np.log10(slopelineF) y = np.log10(psd[fslopeidx]) from sklearn import linear_model lm = linear_model.RANSACRegressor(random_state=42) lm.fit(x[:, np.newaxis], y) slopelineP = lm.predict(x[:, np.newaxis]) psd_flat = y - slopelineP.flatten() mask = (psd_flat / psd_flat.max()) < flatten_thresh psd_flat[mask] = 0 slopes = lm.estimator_.coef_ slopes = slopes[0] return slopes, slopelineP, slopelineF
def spectral_slope(f, psd, f_sampleslope = np.arange(80,200), flatten_thresh = 0): ''' Calculate the slope of the power spectrum NOTE: This is an update to the 'slope' function that should be deprecated Parameters ---------- f : array frequencies corresponding to power spectrum psd : array power spectrum fslopelim : 2-element list frequency range to fit slope flatten_thresh : float See foof.utils Returns ------- slope_value : float slope of psd (chi) fit_logf : array linear fit of the PSD in log-log space (includes information of offset) fit_logpower : array frequency array corresponding to slopebyf ''' # Define frequency and power values to fit in log-log space fit_logf = np.log10(f_sampleslope) y = np.log10(psd[f_sampleslope.astype(int)]) # Fit a linear model lm = linear_model.RANSACRegressor(random_state=42) lm.fit(fit_logf[:, np.newaxis], y) # Find the line of best fit using the provided frequencies fit_logpower = lm.predict(fit_logf[:, np.newaxis]) psd_flat = y - fit_logpower.flatten() mask = (psd_flat / psd_flat.max()) < flatten_thresh psd_flat[mask] = 0 # Find the chi value slope_value = lm.estimator_.coef_ slope_value = slope_value[0] return slope_value, fit_logf, fit_logpower
def estimatenormals(points, npoints = 40, method = 'pca'): """ estimate the normals of points :param points: an array of [x, y, z] :param method: 'pca' or 'ransac', theoretically ransac is more precise when there are more points :return: a list of normal vectors author: weiwei date: 20170714 """ pointsnormals = [] camerapos = np.array([0.0,0.0,0.0]) kdt = KDTree(points) if method == 'pca': regionpntidlist = kdt.query(points, k=npoints, return_distance=False) for i, pntidlist in enumerate(regionpntidlist): regionpnts = points[pntidlist] covmat = np.cov(regionpnts.T) eigvalues, eigmat = np.linalg.eig(covmat) idx = np.argmin(eigvalues) eigvec = eigmat[:, idx] if np.dot(eigvec, camerapos-points[i]) < 0: eigvec = -eigvec pointsnormals.append(eigvec) elif method == 'ransac': # NOTE: this part is not usable due to small npoints ransacer = linear_model.RANSACRegressor(linear_model.LinearRegression()) regionpntidlist = kdt.query(points, k=npoints, return_distance=False) for i, pntidlist in enumerate(regionpntidlist): XYZ = points[pntidlist] ransacer.fit(XYZ[:, 0:2], XYZ[:, 2]) inlier_mask = ransacer.inlier_mask_ regionpnts = XYZ[inlier_mask] covmat = np.cov(regionpnts.T) eigvalues, eigmat = np.linalg.eig(covmat) idx = np.argmin(eigvalues) eigvec = eigmat[:, idx] if np.dot(eigvec, camerapos-points[i]) < 0: eigvec = -eigvec pointsnormals.append(eigvec) return pointsnormals
def test_ransac_fit_sample_weight(): ransac_estimator = RANSACRegressor(random_state=0) n_samples = y.shape[0] weights = np.ones(n_samples) ransac_estimator.fit(X, y, weights) # sanity check assert_equal(ransac_estimator.inlier_mask_.shape[0], n_samples) ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_ ).astype(np.bool_) ref_inlier_mask[outliers] = False # check that mask is correct assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask) # check that fit(X) = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where # X = X1 repeated n1 times, X2 repeated n2 times and so forth random_state = check_random_state(0) X_ = random_state.randint(0, 200, [10, 1]) y_ = np.ndarray.flatten(0.2 * X_ + 2) sample_weight = random_state.randint(0, 10, 10) outlier_X = random_state.randint(0, 1000, [1, 1]) outlier_weight = random_state.randint(0, 10, 1) outlier_y = random_state.randint(-1000, 0, 1) X_flat = np.append(np.repeat(X_, sample_weight, axis=0), np.repeat(outlier_X, outlier_weight, axis=0), axis=0) y_flat = np.ndarray.flatten(np.append(np.repeat(y_, sample_weight, axis=0), np.repeat(outlier_y, outlier_weight, axis=0), axis=0)) ransac_estimator.fit(X_flat, y_flat) ref_coef_ = ransac_estimator.estimator_.coef_ sample_weight = np.append(sample_weight, outlier_weight) X_ = np.append(X_, outlier_X, axis=0) y_ = np.append(y_, outlier_y) ransac_estimator.fit(X_, y_, sample_weight) assert_almost_equal(ransac_estimator.estimator_.coef_, ref_coef_) # check that if base_estimator.fit doesn't support # sample_weight, raises error base_estimator = Lasso() ransac_estimator = RANSACRegressor(base_estimator) assert_raises(ValueError, ransac_estimator.fit, X, y, weights)
