Python numpy.column_stack() Examples
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code examples of numpy.column_stack().
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Example #1
Source Project: pymoo Author: msu-coinlab File: truss2d.py License: Apache License 2.0 | 7 votes |
def _evaluate(self, x, out, *args, **kwargs): # variable names for convenient access x1 = x[:, 0] x2 = x[:, 1] y = x[:, 2] # first objectives f1 = x1 * anp.sqrt(16 + anp.square(y)) + x2 * anp.sqrt((1 + anp.square(y))) # measure which are needed for the second objective sigma_ac = 20 * anp.sqrt(16 + anp.square(y)) / (y * x1) sigma_bc = 80 * anp.sqrt(1 + anp.square(y)) / (y * x2) # take the max f2 = anp.max(anp.column_stack((sigma_ac, sigma_bc)), axis=1) # define a constraint g1 = f2 - self.Smax out["F"] = anp.column_stack([f1, f2]) out["G"] = g1
Example #2
Source Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: metric.py License: Apache License 2.0 | 6 votes |
def update(self, labels, preds): """Updates the internal evaluation result. Parameters ---------- labels : list of `NDArray` The labels of the data. preds : list of `NDArray` Predicted values. """ mx.metric.check_label_shapes(labels, preds) for label, pred in zip(labels, preds): label = label.asnumpy() pred = pred.asnumpy() pred = np.column_stack((1 - pred, pred)) label = label.ravel() num_examples = pred.shape[0] assert label.shape[0] == num_examples, (label.shape[0], num_examples) prob = pred[np.arange(num_examples, dtype=np.int64), np.int64(label)] self.sum_metric += (-np.log(prob + self.eps)).sum() self.num_inst += num_examples
Example #3
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 6 votes |
def _positional_to_optimal(self, K): k, l = self.k, self.l suffix = np.full((len(K), self.l), 0.0) X = np.column_stack([K, suffix]) X[:, self.k + self.l - 1] = 0.35 for i in range(self.k + self.l - 2, self.k - 1, -1): m = X[:, i + 1:k + l] val = m.sum(axis=1) / m.shape[1] X[:, i] = 0.35 ** ((0.02 + 1.96 * val) ** -1) ret = X * (2 * (np.arange(self.n_var) + 1)) return ret # --------------------------------------------------------------------------------------------------------- # TRANSFORMATIONS # ---------------------------------------------------------------------------------------------------------
Example #4
Source Project: pymoo Author: msu-coinlab File: point_crossover.py License: Apache License 2.0 | 6 votes |
def _do(self, problem, X, **kwargs): # get the X of parents and count the matings _, n_matings, n_var = X.shape # start point of crossover r = np.row_stack([np.random.permutation(n_var - 1) + 1 for _ in range(n_matings)])[:, :self.n_points] r.sort(axis=1) r = np.column_stack([r, np.full(n_matings, n_var)]) # the mask do to the crossover M = np.full((n_matings, n_var), False) # create for each individual the crossover range for i in range(n_matings): j = 0 while j < r.shape[1] - 1: a, b = r[i, j], r[i, j + 1] M[i, a:b] = True j += 2 _X = crossover_mask(X, M) return _X
Example #5
Source Project: pymoo Author: msu-coinlab File: performance.py License: Apache License 2.0 | 6 votes |
def geometric_mean_var(z): for row in np.eye(z.shape[1]): if not np.any(np.all(row == z, axis=1)): z = np.row_stack([z, row]) n_points, n_dim = z.shape D = vectorized_cdist(z, z) np.fill_diagonal(D, np.inf) k = n_dim - 1 I = D.argsort(axis=1)[:, :k] first = np.column_stack([np.arange(n_points) for _ in range(k)]) val = gmean(D[first, I], axis=1) return val.var()
Example #6
Source Project: pymoo Author: msu-coinlab File: performance.py License: Apache License 2.0 | 6 votes |
def mean_mean(z): for row in np.eye(z.shape[1]): if not np.any(np.all(row == z, axis=1)): z = np.row_stack([z, row]) n_points, n_dim = z.shape D = vectorized_cdist(z, z) np.fill_diagonal(D, np.inf) k = n_dim - 1 I = D.argsort(axis=1)[:, :k] first = np.column_stack([np.arange(n_points) for _ in range(k)]) val = np.mean(D[first, I], axis=1) return val.mean()
Example #7
Source Project: pymoo Author: msu-coinlab File: reference_direction.py License: Apache License 2.0 | 6 votes |
def map_onto_unit_simplex(rnd, method): n_points, n_dim = rnd.shape if method == "sum": ret = rnd / rnd.sum(axis=1)[:, None] elif method == "kraemer": M = sys.maxsize rnd *= M rnd = rnd[:, :n_dim - 1] rnd = np.column_stack([np.zeros(n_points), rnd, np.full(n_points, M)]) rnd = np.sort(rnd, axis=1) ret = np.full((n_points, n_dim), np.nan) for i in range(1, n_dim + 1): ret[:, i - 1] = rnd[:, i] - rnd[:, i - 1] ret /= M else: raise Exception("Invalid unit simplex mapping!") return ret
Example #8
Source Project: pymoo Author: msu-coinlab File: test_gradient.py License: Apache License 2.0 | 6 votes |
def _evaluate(self, x, out, *args, **kwargs): f1 = x[:, 0] c = np.sum(x[:, 1:], axis=1) g = 1.0 + 9.0 * c / (self.n_var - 1) f2 = g * (1 - np.power(f1 * 1.0 / g, 0.5) - (f1 * 1.0 / g) * np.sin(10 * np.pi * f1)) out["F"] = np.column_stack([f1, f2]) if "dF" in out: dF = np.zeros([x.shape[0], self.n_obj, self.n_var], dtype=np.float) dF[:, 0, 0], dF[:, 0, 1:] = 1, 0 dF[:, 1, 0] = -0.5 * np.sqrt(g / x[:, 0]) - np.sin(10 * np.pi * x[:, 0]) - 10 * np.pi * x[:, 0] * np.cos( 10 * np.pi * x[:, 0]) dF[:, 1, 1:] = (9 / (self.n_var - 1)) * (1 - 0.5 * np.sqrt(x[:, 0] / g))[:, None] out["dF"] = dF
Example #9
Source Project: refractiveindex.info-scripts Author: polyanskiy File: Kaiser 1962 - CaF2.py License: GNU General Public License v3.0 | 6 votes |
def SaveYML(w_um, RefInd, filename, references='', comments=''): header = np.empty(9, dtype=object) header[0] = '# this file is part of refractiveindex.info database' header[1] = '# refractiveindex.info database is in the public domain' header[2] = '# copyright and related rights waived via CC0 1.0' header[3] = '' header[4] = 'REFERENCES:' + references header[5] = 'COMMENTS:' + comments header[6] = 'DATA:' header[7] = ' - type: tabulated nk' header[8] = ' data: |' export = np.column_stack((w_um, np.real(RefInd), np.imag(RefInd))) np.savetxt(filename, export, fmt='%4.2f %#.4g %#.4g', delimiter=' ', header='\n'.join(header), comments='',newline='\n ') return ############################################################################### ## Wavelengths to sample ##
Example #10
Source Project: refractiveindex.info-scripts Author: polyanskiy File: Tsuda 2018 - PMMA (BB model).py License: GNU General Public License v3.0 | 6 votes |
def SaveYML(w_um, RefInd, filename, references='', comments=''): header = np.empty(9, dtype=object) header[0] = '# this file is part of refractiveindex.info database' header[1] = '# refractiveindex.info database is in the public domain' header[2] = '# copyright and related rights waived via CC0 1.0' header[3] = '' header[4] = 'REFERENCES:' + references header[5] = 'COMMENTS:' + comments header[6] = 'DATA:' header[7] = ' - type: tabulated nk' header[8] = ' data: |' export = np.column_stack((w_um, np.real(RefInd), np.imag(RefInd))) np.savetxt(filename, export, fmt='%4.2f %#.4g %#.3e', delimiter=' ', header='\n'.join(header), comments='',newline='\n ') return ############################################################################### ## Wavelengths to sample ##
Example #11
Source Project: refractiveindex.info-scripts Author: polyanskiy File: Zhang 1998 - Kapton.py License: GNU General Public License v3.0 | 6 votes |
def SaveYML(w_um, RefInd, filename, references='', comments=''): header = np.empty(9, dtype=object) header[0] = '# this file is part of refractiveindex.info database' header[1] = '# refractiveindex.info database is in the public domain' header[2] = '# copyright and related rights waived via CC0 1.0' header[3] = '' header[4] = 'REFERENCES:' + references header[5] = 'COMMENTS:' + comments header[6] = 'DATA:' header[7] = ' - type: tabulated nk' header[8] = ' data: |' export = np.column_stack((w_um, np.real(RefInd), np.imag(RefInd))) np.savetxt(filename, export, fmt='%4.3f %#.4g %#.3e', delimiter=' ', header='\n'.join(header), comments='',newline='\n ') return ############################################################################### ## Wavelengths to sample ##
Example #12
Source Project: refractiveindex.info-scripts Author: polyanskiy File: Tsuda 2018 - PMMA (LD model).py License: GNU General Public License v3.0 | 6 votes |
def SaveYML(w_um, RefInd, filename, references='', comments=''): header = np.empty(9, dtype=object) header[0] = '# this file is part of refractiveindex.info database' header[1] = '# refractiveindex.info database is in the public domain' header[2] = '# copyright and related rights waived via CC0 1.0' header[3] = '' header[4] = 'REFERENCES:' + references header[5] = 'COMMENTS:' + comments header[6] = 'DATA:' header[7] = ' - type: tabulated nk' header[8] = ' data: |' export = np.column_stack((w_um, np.real(RefInd), np.imag(RefInd))) np.savetxt(filename, export, fmt='%4.2f %#.4g %#.3e', delimiter=' ', header='\n'.join(header), comments='',newline='\n ') return ############################################################################### ## Wavelengths to sample ##
Example #13
Source Project: refractiveindex.info-scripts Author: polyanskiy File: Kaiser 1962 - BaF2.py License: GNU General Public License v3.0 | 6 votes |
def SaveYML(w_um, RefInd, filename, references='', comments=''): header = np.empty(9, dtype=object) header[0] = '# this file is part of refractiveindex.info database' header[1] = '# refractiveindex.info database is in the public domain' header[2] = '# copyright and related rights waived via CC0 1.0' header[3] = '' header[4] = 'REFERENCES:' + references header[5] = 'COMMENTS:' + comments header[6] = 'DATA:' header[7] = ' - type: tabulated nk' header[8] = ' data: |' export = np.column_stack((w_um, np.real(RefInd), np.imag(RefInd))) np.savetxt(filename, export, fmt='%4.2f %#.4g %#.4g', delimiter=' ', header='\n'.join(header), comments='',newline='\n ') return ############################################################################### ## Wavelengths to sample ##
Example #14
Source Project: feets Author: quatrope File: ext_dmdt.py License: MIT License | 6 votes |
def fit(self, magnitude, time, dt_bins, dm_bins): def delta_calc(idx): t0 = time[idx] m0 = magnitude[idx] deltat = time[idx + 1 :] - t0 deltam = magnitude[idx + 1 :] - m0 deltat[np.where(deltat < 0)] *= -1 deltam[np.where(deltat < 0)] *= -1 return np.column_stack((deltat, deltam)) lc_len = len(time) n_vals = int(0.5 * lc_len * (lc_len - 1)) deltas = np.vstack(tuple(delta_calc(idx) for idx in range(lc_len - 1))) deltat = deltas[:, 0] deltam = deltas[:, 1] bins = [dt_bins, dm_bins] counts = np.histogram2d(deltat, deltam, bins=bins, normed=False)[0] result = np.fix(255.0 * counts / n_vals + 0.999).astype(int) return {"DMDT": result}
Example #15
Source Project: pulse2percept Author: pulse2percept File: beyeler2019.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def calc_axon_contribution(self, axons): xyret = np.column_stack((self.grid.xret.ravel(), self.grid.yret.ravel())) # Only include axon segments that are < `max_d2` from the soma. These # axon segments will have `sensitivity` > `self.min_ax_sensitivity`: max_d2 = -2.0 * self.axlambda ** 2 * np.log(self.min_ax_sensitivity) axon_contrib = [] for xy, bundle in zip(xyret, axons): idx = np.argmin((bundle[:, 0] - xy[0]) ** 2 + (bundle[:, 1] - xy[1]) ** 2) # Cut off the part of the fiber that goes beyond the soma: axon = np.flipud(bundle[0: idx + 1, :]) # Add the exact location of the soma: axon = np.insert(axon, 0, xy, axis=0) # For every axon segment, calculate distance from soma by # summing up the individual distances between neighboring axon # segments (by "walking along the axon"): d2 = np.cumsum(np.diff(axon[:, 0], axis=0) ** 2 + np.diff(axon[:, 1], axis=0) ** 2) idx_d2 = d2 < max_d2 sensitivity = np.exp(-d2[idx_d2] / (2.0 * self.axlambda ** 2)) idx_d2 = np.insert(idx_d2, 0, False) contrib = np.column_stack((axon[idx_d2, :], sensitivity)) axon_contrib.append(contrib) return axon_contrib
Example #16
Source Project: pulse2percept Author: pulse2percept File: test_beyeler2019.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_AxonMapModel_calc_axon_contribution(engine): model = AxonMapModel(xystep=2, engine=engine, n_axons=10, xrange=(-20, 20), yrange=(-15, 15), axons_range=(-30, 30)) model.build() xyret = np.column_stack((model.spatial.grid.xret.ravel(), model.spatial.grid.yret.ravel())) bundles = model.spatial.grow_axon_bundles() axons = model.spatial.find_closest_axon(bundles) contrib = model.spatial.calc_axon_contribution(axons) # Check lambda math: for ax, xy in zip(contrib, xyret): axon = np.insert(ax, 0, list(xy) + [0], axis=0) d2 = np.cumsum(np.diff(axon[:, 0], axis=0) ** 2 + np.diff(axon[:, 1], axis=0) ** 2) sensitivity = np.exp(-d2 / (2.0 * model.spatial.axlambda ** 2)) npt.assert_almost_equal(sensitivity, ax[:, 2])
Example #17
Source Project: Reinforcement_Learning_for_Traffic_Light_Control Author: quantumiracle File: RL_brain.py License: Apache License 2.0 | 6 votes |
def store_transition(self, s, a, r, s_): if not hasattr(self, 'memory_counter'): self.memory_counter = 0 #print(s,s_.size) s=s.reshape(-1) s_=s_.reshape(-1) transition = np.hstack((s, [a, r], s_)) #transition = np.column_stack((s, [a, r], s_)) #transition = np.concatenate((s, [a, r], s_), axis=1) #transition = scipy.sparse.hstack([s, [a, r], s_]).toarray() # replace the old memory with new memory index = self.memory_counter % self.memory_size self.memory[index, :] = transition self.memory_counter += 1
Example #18
Source Project: Reinforcement_Learning_for_Traffic_Light_Control Author: quantumiracle File: RL_brain.py License: Apache License 2.0 | 6 votes |
def store_transition(self, s, a, r, s_): if not hasattr(self, 'memory_counter'): self.memory_counter = 0 #print(s,s_.size) s=s.reshape(-1) s_=s_.reshape(-1) transition = np.hstack((s, [a, r], s_)) #transition = np.column_stack((s, [a, r], s_)) #transition = np.concatenate((s, [a, r], s_), axis=1) #transition = scipy.sparse.hstack([s, [a, r], s_]).toarray() # replace the old memory with new memory index = self.memory_counter % self.memory_size self.memory[index, :] = transition self.memory_counter += 1
Example #19
Source Project: Reinforcement_Learning_for_Traffic_Light_Control Author: quantumiracle File: RL_brain.py License: Apache License 2.0 | 6 votes |
def store_transition(self, s, a, r, s_): self.lo.acquire() s=s.reshape(-1) s_=s_.reshape(-1) transition = np.hstack((s, [a, r], s_)) #transition = np.column_stack((s, [a, r], s_)) #transition = np.concatenate((s, [a, r], s_), axis=1) #transition = scipy.sparse.hstack([s, [a, r], s_]).toarray() # replace the old memory with new memory index = self.memory_counter % self.memory_size self.memory[index, :] = transition self.memory_counter += 1 self.lo.release() # print(index)
Example #20
Source Project: Reinforcement_Learning_for_Traffic_Light_Control Author: quantumiracle File: RL_brain2.py License: Apache License 2.0 | 6 votes |
def store_transition(self, s, a, r, s_): if not hasattr(self, 'memory_counter'): self.memory_counter = 0 #print(s,s_.size) s=s.reshape(-1) s_=s_.reshape(-1) transition = np.hstack((s, [a, r], s_)) #transition = np.column_stack((s, [a, r], s_)) #transition = np.concatenate((s, [a, r], s_), axis=1) #transition = scipy.sparse.hstack([s, [a, r], s_]).toarray() # replace the old memory with new memory index = self.memory_counter % self.memory_size self.memory[index, :] = transition self.memory_counter += 1
Example #21
Source Project: Reinforcement_Learning_for_Traffic_Light_Control Author: quantumiracle File: RL_brain2.py License: Apache License 2.0 | 6 votes |
def store_transition(self, s, a, r, s_): if not hasattr(self, 'memory_counter'): self.memory_counter = 0 #print(s,s_.size) s=s.reshape(-1) s_=s_.reshape(-1) transition = np.hstack((s, [a, r], s_)) #transition = np.column_stack((s, [a, r], s_)) #transition = np.concatenate((s, [a, r], s_), axis=1) #transition = scipy.sparse.hstack([s, [a, r], s_]).toarray() # replace the old memory with new memory index = self.memory_counter % self.memory_size self.memory[index, :] = transition self.memory_counter += 1
Example #22
Source Project: FRIDA Author: LCAV File: tools_fri_doa_plane.py License: MIT License | 5 votes |
def compute_b(G_lst, GtG_lst, beta_lst, Rc0, num_bands, a_ri): """ compute the uniform sinusoidal samples b from the updated annihilating filter coeffiients. :param GtG_lst: list of G^H G for different subbands :param beta_lst: list of beta-s for different subbands :param Rc0: right-dual matrix, here it is the convolution matrix associated with c :param num_bands: number of bands :param L: size of b: L by 1 :param a_ri: a 2D numpy array. each column corresponds to the measurements within a subband :return: """ b_lst = [] a_Gb_lst = [] for loop in range(num_bands): GtG_loop = GtG_lst[loop] beta_loop = beta_lst[loop] b_loop = beta_loop - \ linalg.solve(GtG_loop, np.dot(Rc0.T, linalg.solve(np.dot(Rc0, linalg.solve(GtG_loop, Rc0.T)), np.dot(Rc0, beta_loop))) ) b_lst.append(b_loop) a_Gb_lst.append(a_ri[:, loop] - np.dot(G_lst[loop], b_loop)) return np.column_stack(b_lst), linalg.norm(np.concatenate(a_Gb_lst))
Example #23
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 5 votes |
def _post(self, t, a): x = [] for i in range(t.shape[1] - 1): x.append(np.maximum(t[:, -1], a[i]) * (t[:, i] - 0.5) + 0.5) x.append(t[:, -1]) return np.column_stack(x)
Example #24
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 5 votes |
def _calculate(self, x, s, h): return x[:, -1][:, None] + s * np.column_stack(h)
Example #25
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 5 votes |
def _positional_to_optimal(self, K): suffix = np.full((len(K), self.l), 0.35) X = np.column_stack([K, suffix]) return X * self.xu
Example #26
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 5 votes |
def t4(x, m, n, k): w = np.arange(2, 2 * n + 1, 2) gap = k // (m - 1) t = [] for m in range(1, m): _y = x[:, (m - 1) * gap: (m * gap)] _w = w[(m - 1) * gap: (m * gap)] t.append(_reduction_weighted_sum(_y, _w)) t.append(_reduction_weighted_sum(x[:, k:n], w[k:n])) return np.column_stack(t)
Example #27
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 5 votes |
def t2(x, n, k): y = [x[:, i] for i in range(k)] l = n - k ind_non_sep = k + l // 2 i = k + 1 while i <= ind_non_sep: head = k + 2 * (i - k) - 2 tail = k + 2 * (i - k) y.append(_reduction_non_sep(x[:, head:tail], 2)) i += 1 return np.column_stack(y)
Example #28
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 5 votes |
def t2(x, m, k): gap = k // (m - 1) t = [_reduction_weighted_sum_uniform(x[:, (m - 1) * gap: (m * gap)]) for m in range(1, m)] t.append(_reduction_weighted_sum_uniform(x[:, k:])) return np.column_stack(t)
Example #29
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 5 votes |
def t2(x, m, n, k): gap = k // (m - 1) t = [_reduction_non_sep(x[:, (m - 1) * gap: (m * gap)], gap) for m in range(1, m)] t.append(_reduction_non_sep(x[:, k:], n - k)) return np.column_stack(t)
Example #30
Source Project: pymoo Author: msu-coinlab File: wfg.py License: Apache License 2.0 | 5 votes |
def t1(x, n, k): ret = [] for i in range(k, n): aux = _reduction_weighted_sum_uniform(x[:, :i]) ret.append(_transformation_param_dependent(x[:, i], aux, A=0.98 / 49.98, B=0.02, C=50.0)) return np.column_stack(ret)