Python numpy.log1p() Examples
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Example #1
Source File: test_preprocessing.py From scanpy with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_log1p(tmp_path): A = np.random.rand(200, 10) A_l = np.log1p(A) ad = AnnData(A) ad2 = AnnData(A) ad3 = AnnData(A) ad3.filename = tmp_path / 'test.h5ad' sc.pp.log1p(ad) assert np.allclose(ad.X, A_l) sc.pp.log1p(ad2, chunked=True) assert np.allclose(ad2.X, ad.X) sc.pp.log1p(ad3, chunked=True) assert np.allclose(ad3.X, ad.X) # Test base ad4 = AnnData(A) sc.pp.log1p(ad4, base=2) assert np.allclose(ad4.X, A_l/np.log(2))
Example #2
Source File: log_loss_weighted.py From risk-slim with BSD 3-Clause "New" or "Revised" License | 6 votes |
def log_loss_value(Z, weights, total_weights, rho): """ computes the value and slope of the logistic loss in a numerically stable way supports sample non-negative weights for each example in the training data see http://stackoverflow.com/questions/20085768/ Parameters ---------- Z numpy.array containing training data with shape = (n_rows, n_cols) rho numpy.array of coefficients with shape = (n_cols,) total_weights numpy.sum(total_weights) (only included to reduce computation) weights numpy.array of sample weights with shape (n_rows,) Returns ------- loss_value scalar = 1/n_rows * sum(log( 1 .+ exp(-Z*rho)) """ scores = Z.dot(rho) pos_idx = scores > 0 loss_value = np.empty_like(scores) loss_value[pos_idx] = np.log1p(np.exp(-scores[pos_idx])) loss_value[~pos_idx] = -scores[~pos_idx] + np.log1p(np.exp(scores[~pos_idx])) loss_value = loss_value.dot(weights) / total_weights return loss_value
Example #3
Source File: test_umath.py From recruit with Apache License 2.0 | 6 votes |
def test_branch_cuts(self): # check branch cuts and continuity on them _check_branch_cut(np.log, -0.5, 1j, 1, -1, True) _check_branch_cut(np.log2, -0.5, 1j, 1, -1, True) _check_branch_cut(np.log10, -0.5, 1j, 1, -1, True) _check_branch_cut(np.log1p, -1.5, 1j, 1, -1, True) _check_branch_cut(np.sqrt, -0.5, 1j, 1, -1, True) _check_branch_cut(np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True) _check_branch_cut(np.arccos, [ -2, 2], [1j, 1j], 1, -1, True) _check_branch_cut(np.arctan, [0-2j, 2j], [1, 1], -1, 1, True) _check_branch_cut(np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True) _check_branch_cut(np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True) _check_branch_cut(np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True) # check against bogus branch cuts: assert continuity between quadrants _check_branch_cut(np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1) _check_branch_cut(np.arccos, [0-2j, 2j], [ 1, 1], 1, 1) _check_branch_cut(np.arctan, [ -2, 2], [1j, 1j], 1, 1) _check_branch_cut(np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1) _check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1) _check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1)
Example #4
Source File: test_umath.py From recruit with Apache License 2.0 | 6 votes |
def test_branch_cuts_complex64(self): # check branch cuts and continuity on them _check_branch_cut(np.log, -0.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.log2, -0.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.log10, -0.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.log1p, -1.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.sqrt, -0.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64) _check_branch_cut(np.arccos, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64) _check_branch_cut(np.arctan, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64) _check_branch_cut(np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64) _check_branch_cut(np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True, np.complex64) _check_branch_cut(np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64) # check against bogus branch cuts: assert continuity between quadrants _check_branch_cut(np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64) _check_branch_cut(np.arccos, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64) _check_branch_cut(np.arctan, [ -2, 2], [1j, 1j], 1, 1, False, np.complex64) _check_branch_cut(np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1, False, np.complex64) _check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1, False, np.complex64) _check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1, False, np.complex64)
Example #5
Source File: log_loss.py From risk-slim with BSD 3-Clause "New" or "Revised" License | 6 votes |
def log_loss_value(Z, rho): """ computes the value and slope of the logistic loss in a numerically stable way see also: http://stackoverflow.com/questions/20085768/ Parameters ---------- Z numpy.array containing training data with shape = (n_rows, n_cols) rho numpy.array of coefficients with shape = (n_cols,) Returns ------- loss_value scalar = 1/n_rows * sum(log( 1 .+ exp(-Z*rho)) """ scores = Z.dot(rho) pos_idx = scores > 0 loss_value = np.empty_like(scores) loss_value[pos_idx] = np.log1p(np.exp(-scores[pos_idx])) loss_value[~pos_idx] = -scores[~pos_idx] + np.log1p(np.exp(scores[~pos_idx])) loss_value = loss_value.mean() return loss_value
Example #6
Source File: process.py From scanorama with MIT License | 6 votes |
def load_names(data_names, norm=True, log1p=False, verbose=True): # Load datasets. datasets = [] genes_list = [] n_cells = 0 for name in data_names: X_i, genes_i = load_data(name) if norm: X_i = normalize(X_i, axis=1) if log1p: X_i = np.log1p(X_i) X_i = csr_matrix(X_i) datasets.append(X_i) genes_list.append(genes_i) n_cells += X_i.shape[0] if verbose: print('Loaded {} with {} genes and {} cells'. format(name, X_i.shape[1], X_i.shape[0])) if verbose: print('Found {} cells among all datasets' .format(n_cells)) return datasets, genes_list, n_cells
Example #7
Source File: process.py From geosketch with MIT License | 6 votes |
def load_names(data_names, norm=True, log1p=False, verbose=True): # Load datasets. datasets = [] genes_list = [] n_cells = 0 for name in data_names: X_i, genes_i = load_data(name) if norm: X_i = normalize(X_i, axis=1) if log1p: X_i = np.log1p(X_i) X_i = csr_matrix(X_i) datasets.append(X_i) genes_list.append(genes_i) n_cells += X_i.shape[0] if verbose: print('Loaded {} with {} genes and {} cells'. format(name, X_i.shape[1], X_i.shape[0])) if verbose: print('Found {} cells among all datasets' .format(n_cells)) return datasets, genes_list, n_cells
Example #8
Source File: test_umath.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def test_branch_cuts(self): # check branch cuts and continuity on them yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arccos, [ -2, 2], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arctan, [0-2j, 2j], [1, 1], -1, 1, True yield _check_branch_cut, np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True # check against bogus branch cuts: assert continuity between quadrants yield _check_branch_cut, np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1 yield _check_branch_cut, np.arccos, [0-2j, 2j], [ 1, 1], 1, 1 yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1 yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1 yield _check_branch_cut, np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1 yield _check_branch_cut, np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1
Example #9
Source File: test_umath.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def test_branch_cuts_complex64(self): # check branch cuts and continuity on them yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arccos, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arctan, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64 yield _check_branch_cut, np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64 yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 # check against bogus branch cuts: assert continuity between quadrants yield _check_branch_cut, np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arccos, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1, False, np.complex64 yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1, False, np.complex64 yield _check_branch_cut, np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1, False, np.complex64
Example #10
Source File: sigm.py From D-VAE with MIT License | 6 votes |
def c_code(self, node, name, inp, out, sub): x, = inp z, = out # These constants were obtained by looking at the output of # python commands like: # for i in xrange(750): # print i, repr(numpy.log1p(numpy.exp(theano._asarray([i,-i], dtype=dt)))) # the boundary checks prevent us from generating inf # float16 limits: -17.0, 6.0 # We use the float32 limits for float16 for now as the # computation will happend in float32 anyway. if (node.inputs[0].type == scalar.float32 or node.inputs[0].type == scalar.float16): return """%(z)s = %(x)s < -103.0f ? 0.0 : %(x)s > 14.0f ? %(x)s : log1p(exp(%(x)s));""" % locals() elif node.inputs[0].type == scalar.float64: return """%(z)s = %(x)s < -745.0 ? 0.0 : %(x)s > 16.0 ? %(x)s : log1p(exp(%(x)s));""" % locals() else: raise NotImplementedError('only floatingpoint is implemented')
Example #11
Source File: conftest.py From Kaggler with MIT License | 6 votes |
def generate_data(): generated = False def _generate_data(): if not generated: assert N_CAT_FEATURE > 1 assert N_NUM_FEATURE > 3 np.random.seed(RANDOM_SEED) X_num = np.random.normal(size=(N_OBS, N_NUM_FEATURE)) X_cat = np.random.randint(0, N_CATEGORY, size=(N_OBS, N_CAT_FEATURE)) df = pd.DataFrame( np.hstack((X_num, X_cat)), columns=['num_{}'.format(x) for x in range(N_NUM_FEATURE)] + ['cat_{}'.format(x) for x in range(N_CAT_FEATURE)] ) df[TARGET_COL] = (1 + X_num[:, 0] * X_num[:, 1] - np.log1p(np.exp(X_num[:, 1] + X_num[:, 2])) + 10 * (X_cat[:, 0] == 0).astype(int) + np.random.normal(scale=.01, size=N_OBS)) return df yield _generate_data
Example #12
Source File: bow_stats.py From tmtoolkit with Apache License 2.0 | 6 votes |
def idf(dtm, smooth_log=1, smooth_df=1): """ Calculate inverse document frequency (idf) vector from raw count document-term-matrix `dtm` with formula ``log(smooth_log + N / (smooth_df + df))``, where ``N`` is the number of documents, ``df`` is the document frequency (see function :func:`~tmtoolkit.bow.bow_stats.doc_frequencies`), `smooth_log` and `smooth_df` are smoothing constants. With default arguments, the formula is thus ``log(1 + N/(1+df))``. Note that this may introduce NaN values due to division by zero when a document is of length 0. :param dtm: (sparse) document-term-matrix of size NxM (N docs, M is vocab size) with raw term counts. :param smooth_log: smoothing constant inside log() :param smooth_df: smoothing constant to add to document frequency :return: NumPy array of size M (vocab size) with inverse document frequency for each term in the vocab """ if dtm.ndim != 2 or 0 in dtm.shape: raise ValueError('`dtm` must be a non-empty 2D array/matrix') n_docs = dtm.shape[0] df = doc_frequencies(dtm) x = n_docs / (smooth_df + df) if smooth_log == 1: # log1p is faster than the equivalent log(1 + x) return np.log1p(x) else: return np.log(smooth_log + x)
Example #13
Source File: nearest_neighbours.py From implicit with MIT License | 6 votes |
def bm25_weight(X, K1=100, B=0.8): """ Weighs each row of a sparse matrix X by BM25 weighting """ # calculate idf per term (user) X = coo_matrix(X) N = float(X.shape[0]) idf = log(N) - log1p(bincount(X.col)) # calculate length_norm per document (artist) row_sums = numpy.ravel(X.sum(axis=1)) average_length = row_sums.mean() length_norm = (1.0 - B) + B * row_sums / average_length # weight matrix rows by bm25 X.data = X.data * (K1 + 1.0) / (K1 * length_norm[X.row] + X.data) * idf[X.col] return X
Example #14
Source File: bow_stats.py From tmtoolkit with Apache License 2.0 | 6 votes |
def tf_log(dtm, log_fn=np.log1p): """ Transform raw count document-term-matrix `dtm` to log-normalized term frequency matrix ``log_fn(dtm)``. :param dtm: (sparse) document-term-matrix of size NxM (N docs, M is vocab size) with raw term counts. :param log_fn: log function to use; default is NumPy's :func:`numpy.log1p`, which calculates ``log(1 + x)`` :return: (sparse) log-normalized term frequency matrix of size NxM """ if dtm.ndim != 2: raise ValueError('`dtm` must be a 2D array/matrix') if log_fn is np.log1p: if issparse(dtm): return dtm.log1p() else: return log_fn(dtm) else: if issparse(dtm): dtm = dtm.toarray() return log_fn(dtm)
Example #15
Source File: cosh_loss.py From driverlessai-recipes with Apache License 2.0 | 6 votes |
def score(self, actual: np.array, predicted: np.array, sample_weight: typing.Optional[np.array] = None, labels: typing.Optional[np.array] = None, **kwargs) -> float: if sample_weight is None: sample_weight = np.ones(actual.shape[0]) predicted = predicted.ravel() good_rows = predicted >= 0 if not good_rows.any(): return 30 delta = predicted[good_rows] - actual[good_rows] sample_weight = sample_weight[good_rows] loss = np.log1p(np.cosh(delta)) return np.sum(sample_weight * loss) / np.sum(sample_weight)
Example #16
Source File: data.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def _yeo_johnson_optimize(self, x): """Find and return optimal lambda parameter of the Yeo-Johnson transform by MLE, for observed data x. Like for Box-Cox, MLE is done via the brent optimizer. """ def _neg_log_likelihood(lmbda): """Return the negative log likelihood of the observed data x as a function of lambda.""" x_trans = self._yeo_johnson_transform(x, lmbda) n_samples = x.shape[0] loglike = -n_samples / 2 * np.log(x_trans.var()) loglike += (lmbda - 1) * (np.sign(x) * np.log1p(np.abs(x))).sum() return -loglike # the computation of lambda is influenced by NaNs so we need to # get rid of them x = x[~np.isnan(x)] # choosing bracket -2, 2 like for boxcox return optimize.brent(_neg_log_likelihood, brack=(-2, 2))
Example #17
Source File: test_umath.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_branch_cuts_complex64(self): # check branch cuts and continuity on them _check_branch_cut(np.log, -0.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.log2, -0.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.log10, -0.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.log1p, -1.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.sqrt, -0.5, 1j, 1, -1, True, np.complex64) _check_branch_cut(np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64) _check_branch_cut(np.arccos, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64) _check_branch_cut(np.arctan, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64) _check_branch_cut(np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64) _check_branch_cut(np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True, np.complex64) _check_branch_cut(np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64) # check against bogus branch cuts: assert continuity between quadrants _check_branch_cut(np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64) _check_branch_cut(np.arccos, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64) _check_branch_cut(np.arctan, [ -2, 2], [1j, 1j], 1, 1, False, np.complex64) _check_branch_cut(np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1, False, np.complex64) _check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1, False, np.complex64) _check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1, False, np.complex64)
Example #18
Source File: test_umath.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_branch_cuts(self): # check branch cuts and continuity on them _check_branch_cut(np.log, -0.5, 1j, 1, -1, True) _check_branch_cut(np.log2, -0.5, 1j, 1, -1, True) _check_branch_cut(np.log10, -0.5, 1j, 1, -1, True) _check_branch_cut(np.log1p, -1.5, 1j, 1, -1, True) _check_branch_cut(np.sqrt, -0.5, 1j, 1, -1, True) _check_branch_cut(np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True) _check_branch_cut(np.arccos, [ -2, 2], [1j, 1j], 1, -1, True) _check_branch_cut(np.arctan, [0-2j, 2j], [1, 1], -1, 1, True) _check_branch_cut(np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True) _check_branch_cut(np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True) _check_branch_cut(np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True) # check against bogus branch cuts: assert continuity between quadrants _check_branch_cut(np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1) _check_branch_cut(np.arccos, [0-2j, 2j], [ 1, 1], 1, 1) _check_branch_cut(np.arctan, [ -2, 2], [1j, 1j], 1, 1) _check_branch_cut(np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1) _check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1) _check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1)
Example #19
Source File: test_umath.py From Computable with MIT License | 6 votes |
def test_branch_cuts(self): # check branch cuts and continuity on them yield _check_branch_cut, np.log, -0.5, 1j, 1, -1 yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1 yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1 yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1 yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1 yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, -1j], 1, -1 yield _check_branch_cut, np.arccos, [ -2, 2], [1j, -1j], 1, -1 yield _check_branch_cut, np.arctan, [-2j, 2j], [1, -1 ], -1, 1 yield _check_branch_cut, np.arcsinh, [-2j, 2j], [-1, 1], -1, 1 yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1 yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, -1j], 1, -1 # check against bogus branch cuts: assert continuity between quadrants yield _check_branch_cut, np.arcsin, [-2j, 2j], [ 1, 1], 1, 1 yield _check_branch_cut, np.arccos, [-2j, 2j], [ 1, 1], 1, 1 yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1 yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1 ], 1, 1 yield _check_branch_cut, np.arccosh, [-2j, 2j, 2], [1, 1, 1j], 1, 1 yield _check_branch_cut, np.arctanh, [-2j, 2j, 0], [1, 1, 1j], 1, 1
Example #20
Source File: utils.py From vq-vae with Apache License 2.0 | 6 votes |
def mu_law_encode(audio): '''Quantizes waveform amplitudes. Mostly adaped from https://github.com/ibab/tensorflow-wavenet/blob/master/wavenet/ops.py#L64-L75 Args: audio: Raw wave signal. float32. ''' mu = float(hp.Q - 1) # Perform mu-law companding transformation (ITU-T, 1988). # Minimum operation is here to deal with rare large amplitudes caused # by resampling. magnitude = np.log1p(mu * np.abs(audio)) / np.log1p(mu) signal = np.sign(audio) * magnitude # Quantize signal to the specified number of levels. return ((signal + 1) / 2 * mu + 0.5).astype(np.int32)
Example #21
Source File: test_umath.py From vnpy_crypto with MIT License | 6 votes |
def test_branch_cuts_complex64(self): # check branch cuts and continuity on them yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True, np.complex64 yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arccos, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arctan, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64 yield _check_branch_cut, np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True, np.complex64 yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True, np.complex64 yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True, np.complex64 # check against bogus branch cuts: assert continuity between quadrants yield _check_branch_cut, np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arccos, [0-2j, 2j], [ 1, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1, False, np.complex64 yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1, False, np.complex64 yield _check_branch_cut, np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1, False, np.complex64 yield _check_branch_cut, np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1, False, np.complex64
Example #22
Source File: test_umath.py From vnpy_crypto with MIT License | 6 votes |
def test_branch_cuts(self): # check branch cuts and continuity on them yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arccos, [ -2, 2], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arctan, [0-2j, 2j], [1, 1], -1, 1, True yield _check_branch_cut, np.arcsinh, [0-2j, 2j], [1, 1], -1, 1, True yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, 1j], 1, -1, True # check against bogus branch cuts: assert continuity between quadrants yield _check_branch_cut, np.arcsin, [0-2j, 2j], [ 1, 1], 1, 1 yield _check_branch_cut, np.arccos, [0-2j, 2j], [ 1, 1], 1, 1 yield _check_branch_cut, np.arctan, [ -2, 2], [1j, 1j], 1, 1 yield _check_branch_cut, np.arcsinh, [ -2, 2, 0], [1j, 1j, 1], 1, 1 yield _check_branch_cut, np.arccosh, [0-2j, 2j, 2], [1, 1, 1j], 1, 1 yield _check_branch_cut, np.arctanh, [0-2j, 2j, 0], [1, 1, 1j], 1, 1
Example #23
Source File: bow_stats.py From tmtoolkit with Apache License 2.0 | 6 votes |
def idf_probabilistic(dtm, smooth=1): """ Calculate probabilistic inverse document frequency (idf) vector from raw count document-term-matrix `dtm` with formula ``log(smooth + (N - df) / df)``, where ``N`` is the number of documents and ``df`` is the document frequency (see function :func:`~tmtoolkit.bow.bow_stats.doc_frequencies`). :param dtm: (sparse) document-term-matrix of size NxM (N docs, M is vocab size) with raw term counts. :param smooth: smoothing constant (setting this to 0 can lead to -inf results) :return: NumPy array of size M (vocab size) with probabilistic inverse document frequency for each term in the vocab """ if dtm.ndim != 2 or 0 in dtm.shape: raise ValueError('`dtm` must be a non-empty 2D array/matrix') n_docs = dtm.shape[0] df = doc_frequencies(dtm) x = (n_docs - df) / df if smooth == 1: # log1p is faster than the equivalent log(1 + x) return np.log1p(x) else: return np.log(smooth + x)
Example #24
Source File: main.py From tensorflow-XNN with MIT License | 5 votes |
def load_train_data(): types_dict_train = { 'train_id': 'int32', 'item_condition_id': 'int32', 'price': 'float32', 'shipping': 'int8', 'name': 'str', 'brand_name': 'str', 'item_desc': 'str', 'category_name': 'str', } df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train) df.rename(columns={"train_id": "id"}, inplace=True) df.rename(columns={"item_description": "item_desc"}, inplace=True) if DROP_ZERO_PRICE: df = df[df.price > 0].copy() price = np.log1p(df.price.values) df.drop("price", axis=1, inplace=True) df["price"] = price df["is_train"] = 1 df["missing_brand_name"] = df["brand_name"].isnull().astype(int) df["missing_category_name"] = df["category_name"].isnull().astype(int) missing_ind = np.logical_or(df["item_desc"].isnull(), df["item_desc"].str.lower().str.contains("no\s+description\s+yet")) df["missing_item_desc"] = missing_ind.astype(int) df["item_desc"][missing_ind] = df["name"][missing_ind] gc.collect() if DEBUG: return df.head(DEBUG_SAMPLE_NUM) else: return df
Example #25
Source File: test_basic.py From Computable with MIT License | 5 votes |
def test_log1p(self): l1p = (special.log1p(10), special.log1p(11), special.log1p(12)) l1prl = (log(11), log(12), log(13)) assert_array_almost_equal(l1p,l1prl,8)
Example #26
Source File: test_basic.py From Computable with MIT License | 5 votes |
def test_log1p(self): assert_equal(cephes.log1p(0),0.0)
Example #27
Source File: test_umath.py From Computable with MIT License | 5 votes |
def test_branch_cuts_failing(self): # XXX: signed zero not OK with ICC on 64-bit platform for log, see # http://permalink.gmane.org/gmane.comp.python.numeric.general/25335 yield _check_branch_cut, np.log, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log2, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log10, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.log1p, -1.5, 1j, 1, -1, True # XXX: signed zeros are not OK for sqrt or for the arc* functions yield _check_branch_cut, np.sqrt, -0.5, 1j, 1, -1, True yield _check_branch_cut, np.arcsin, [ -2, 2], [1j, -1j], 1, -1, True yield _check_branch_cut, np.arccos, [ -2, 2], [1j, -1j], 1, -1, True yield _check_branch_cut, np.arctan, [-2j, 2j], [1, -1 ], -1, 1, True yield _check_branch_cut, np.arcsinh, [-2j, 2j], [-1, 1], -1, 1, True yield _check_branch_cut, np.arccosh, [ -1, 0.5], [1j, 1j], 1, -1, True yield _check_branch_cut, np.arctanh, [ -2, 2], [1j, -1j], 1, -1, True
Example #28
Source File: akita_sat_vcf.py From basenji with Apache License 2.0 | 5 votes |
def score_matrix(seq_1hot, sat_preds): # sat_preds is (1 + 3*mut_len) x (target_len) x (num_targets) num_preds = sat_preds.shape[0] num_targets = sat_preds.shape[-1] # reverse engineer mutagenesis position parameters mut_len = (num_preds - 1) // 3 mut_mid = seq_1hot.shape[0] // 2 mut_start = mut_mid - mut_len//2 mut_end = mut_start + mut_len # mutagenized DNA seq_1hot_mut = seq_1hot[mut_start:mut_end,:] # initialize scores seq_scores = np.zeros((mut_len, 4, num_targets), dtype='float32') # predictions index (starting at first mutagenesis) pi = 1 # for each mutated position for mi in range(mut_len): # for each nucleotide for ni in range(4): if seq_1hot_mut[mi,ni]: # reference score seq_scores[mi,ni,:] = 0 else: # mutation score seq_scores[mi,ni,:] = ((sat_preds[pi] - sat_preds[0])**2).sum(axis=0) pi += 1 # transform seq_scores = np.log1p(np.sqrt(seq_scores)) return seq_scores
Example #29
Source File: test_umath.py From Computable with MIT License | 5 votes |
def test_log1p(self): assert_almost_equal(ncu.log1p(0.2), ncu.log(1.2)) assert_almost_equal(ncu.log1p(1e-6), ncu.log(1+1e-6))
Example #30
Source File: pipline.py From MachineLearning with Apache License 2.0 | 5 votes |
def get_input(): train = pd.read_csv('../input/train.csv') test = pd.read_csv('../input/test.csv') y_train_log = np.log1p(train['target']) id_test = test['ID'] del test['ID'] del train['ID'] del train['target'] return train.values, y_train_log.values, test.values, id_test.values