我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.exp()。
def svgd_kernel(self, h = -1): sq_dist = pdist(self.theta) pairwise_dists = squareform(sq_dist)**2 if h < 0: # if h < 0, using median trick h = np.median(pairwise_dists) h = np.sqrt(0.5 * h / np.log(self.theta.shape[0]+1)) # compute the rbf kernel Kxy = np.exp( -pairwise_dists / h**2 / 2) dxkxy = -np.matmul(Kxy, self.theta) sumkxy = np.sum(Kxy, axis=1) for i in range(self.theta.shape[1]): dxkxy[:, i] = dxkxy[:,i] + np.multiply(self.theta[:,i],sumkxy) dxkxy = dxkxy / (h**2) return (Kxy, dxkxy)
def lr_grad_norm_avg(self): # this is for enforcing lr * grad_norm not # increasing dramatically in case of instability. # Not necessary for basic use. global_state = self._global_state beta = self._beta if "lr_grad_norm_avg" not in global_state: global_state['grad_norm_squared_avg_log'] = 0.0 global_state['grad_norm_squared_avg_log'] = \ global_state['grad_norm_squared_avg_log'] * beta \ + (1 - beta) * np.log(global_state['grad_norm_squared'] + eps) if "lr_grad_norm_avg" not in global_state: global_state["lr_grad_norm_avg"] = \ 0.0 * beta + (1 - beta) * np.log(self._lr * np.sqrt(global_state['grad_norm_squared'] ) + eps) # we monitor the minimal smoothed ||lr * grad|| global_state["lr_grad_norm_avg_min"] = \ np.exp(global_state["lr_grad_norm_avg"] / self.zero_debias_factor() ) else: global_state["lr_grad_norm_avg"] = global_state["lr_grad_norm_avg"] * beta \ + (1 - beta) * np.log(self._lr * np.sqrt(global_state['grad_norm_squared'] ) + eps) global_state["lr_grad_norm_avg_min"] = \ min(global_state["lr_grad_norm_avg_min"], np.exp(global_state["lr_grad_norm_avg"] / self.zero_debias_factor() ) )
def update_hyper_param(self): for group in self._optimizer.param_groups: group['momentum'] = self._mu_t #group['momentum'] = max(self._mu, self._mu_t) if self._force_non_inc_step == False: group['lr'] = self._lr_t * self._lr_factor # a loose clamping to prevent catastrophically large move. If the move # is too large, we set lr to 0 and only use the momentum to move if self._adapt_clip and (group['lr'] * np.sqrt(self._global_state['grad_norm_squared']) >= self._catastrophic_move_thresh): group['lr'] = self._catastrophic_move_thresh / np.sqrt(self._global_state['grad_norm_squared'] + eps) if self._verbose: logging.warning("clip catastropic move!") elif self._iter > self._curv_win_width: # force to guarantee lr * grad_norm not increasing dramatically. # Not necessary for basic use. Please refer to the comments # in YFOptimizer.__init__ for more details self.lr_grad_norm_avg() debias_factor = self.zero_debias_factor() group['lr'] = min(self._lr * self._lr_factor, 2.0 * self._global_state["lr_grad_norm_avg_min"] \ / (np.sqrt(np.exp(self._global_state['grad_norm_squared_avg_log'] / debias_factor) ) + eps) ) return
def test_quantize_from_probs2(size, resolution): set_random_seed(make_seed(size, resolution)) probs = np.exp(np.random.random(size)).astype(np.float32) probs2 = probs.reshape((1, size)) quantized = quantize_from_probs2(probs2, resolution) assert quantized.shape == probs2.shape assert quantized.dtype == np.int8 assert np.all(quantized.sum(axis=1) == resolution) # Check that quantized result is closer to target than any other value. quantized = quantized.reshape((size, )) target = resolution * probs / probs.sum() distance = np.abs(quantized - target).sum() for combo in itertools.combinations(range(size), resolution): other = np.zeros(size, np.int8) for i in combo: other[i] += 1 assert other.sum() == resolution other_distance = np.abs(other - target).sum() assert distance <= other_distance
def observed_perplexity(self, counts): """Compute perplexity = exp(entropy) of observed variables. Perplexity is an information theoretic measure of the number of clusters or observed classes. Perplexity is a real number in the range [1, dim[v]], where dim[v] is the number of categories in an observed categorical variable or 2 for an ordinal variable. Args: counts: A [V]-shaped array of multinomial counts. Returns: A [V]-shaped numpy array of perplexity. """ result = self._ensemble[0].observed_perplexity(counts) for server in self._ensemble[1:]: result += server.observed_perplexity(counts) result /= len(self._ensemble) return result
def latent_correlation(self): """Compute correlation matrix among latent features. This computes the generalization of Pearson's correlation to discrete data. Let I(X;Y) be the mutual information. Then define correlation as rho(X,Y) = sqrt(1 - exp(-2 I(X;Y))) Returns: A [V, V]-shaped numpy array of feature-feature correlations. """ result = self._ensemble[0].latent_correlation() for server in self._ensemble[1:]: result += server.latent_correlation() result /= len(self._ensemble) return result
def begin(self): x = random.randint(self.x_range[0], self.x_range[1]) f = self.func(x) T = self.T0 while T > self.T_min: for i in range(self.K): new_x = self.gen_new_x(x, T) f_x = self.func(new_x) delta_E = f_x - f # if delta_E < 0: f = f_x x = new_x break else: #p_k = 1.0 / (1 + np.exp(- delta_E / self.func(T))) p_k = np.exp(- delta_E / T) if random.random() < p_k: f = f_x x = new_x break T *= self.delta return x
def optSigma2(self, U, s, y, covars, logdetXX, reml, ldeltamin=-5, ldeltamax=5): #Prepare required matrices Uy = U.T.dot(y).flatten() UX = U.T.dot(covars) if (U.shape[1] < U.shape[0]): UUX = covars - U.dot(UX) UUy = y - U.dot(Uy) UUXUUX = UUX.T.dot(UUX) UUXUUy = UUX.T.dot(UUy) UUyUUy = UUy.T.dot(UUy) else: UUXUUX, UUXUUy, UUyUUy = None, None, None numIndividuals = U.shape[0] ldeltaopt_glob = optimize.minimize_scalar(self.negLLevalLong, bounds=(-5, 5), method='Bounded', args=(s, Uy, UX, logdetXX, UUXUUX, UUXUUy, UUyUUy, numIndividuals, reml)).x ll, sig2g, beta, r2 = self.negLLevalLong(ldeltaopt_glob, s, Uy, UX, logdetXX, UUXUUX, UUXUUy, UUyUUy, numIndividuals, reml, returnAllParams=True) sig2e = np.exp(ldeltaopt_glob) * sig2g return sig2g, sig2e, beta, ll
def deriveKernel(self, params, i): self.checkParamsI(params, i) c = np.exp(params[-1]) #K = self.kernel.getTrainKernel(params[:-1]) #deriv1 = self.polyDegree * (c+K)**(self.polyDegree-1) K = self.kernel.getTrainKernel(params[:-1]) + c if (self.polyDegree==2): Kpower=K else: Kpower=K**2 for i in xrange(self.polyDegree-3): Kpower*=K #this is faster than direct power deriv1 = self.polyDegree * Kpower if (i==params.shape[0]-1): K_deriv = deriv1*c else: K_deriv = deriv1 * self.kernel.deriveKernel(params[:-1], i) return K_deriv
def getTrainKernel(self, params): self.checkParams(params) if (self.sameParams(params)): return self.cache['getTrainKernel'] ell = np.exp(params[0]) if (self.K_sq is None): K = sq_dist(self.X_scaled.T / ell) #precompute squared distances else: K = self.K_sq / ell**2 self.cache['K_sq_scaled'] = K # # # #manual computation (just for sanity checks) # # # K1 = np.exp(-K / 2.0) # # # K2 = np.zeros((self.X_scaled.shape[0], self.X_scaled.shape[0])) # # # for i1 in xrange(self.X_scaled.shape[0]): # # # for i2 in xrange(i1, self.X_scaled.shape[0]): # # # diff = self.X_scaled[i1,:] - self.X_scaled[i2,:] # # # K2[i1, i2] = np.exp(-np.sum(diff**2) / (2*ell)) # # # K2[i2, i1] = K2[i1, i2] # # # print np.max((K1-K2)**2) # # # sys.exit(0) K_exp = np.exp(-K / 2.0) self.cache['getTrainKernel'] = K_exp self.saveParams(params) return K_exp
def getTrainTestKernel(self, params, Xtest): self.checkParams(params) ell = np.exp(params[0]) p = np.exp(params[1]) Xtest_scaled = Xtest/np.sqrt(Xtest.shape[1]) d2 = sq_dist(self.X_scaled.T/ell, Xtest_scaled.T/ell) #precompute squared distances #compute dp dp = np.zeros(d2.shape) for d in xrange(self.X_scaled.shape[1]): dp += (np.outer(self.X_scaled[:,d], np.ones((1, Xtest_scaled.shape[0]))) - np.outer(np.ones((self.X_scaled.shape[0], 1)), Xtest_scaled[:,d])) dp /= p K = np.exp(-d2 / 2.0) return np.cos(2*np.pi*dp)*K
def getTrainKernel(self, params): self.checkParams(params) if (self.sameParams(params)): return self.cache['getTrainKernel'] if ('K_sq_scaled' not in self.cache.keys()): self.cache['K_sq_scaled'] = [None for i in xrange(self.getNumParams())] ell = np.exp(params) K = 0 for i in xrange(self.getNumParams()): if (self.sameParams(params, i)): K += self.cache['K_sq_scaled'][i] else: self.cache['K_sq_scaled'][i] = self.K_sq[i] / ell[i]**2 K += self.cache['K_sq_scaled'][i] K_exp = np.exp(-K / 2.0) self.cache['getTrainKernel'] = K_exp self.saveParams(params) return K_exp
def getTrainTestKernel(self, params, Xtest): self.checkParams(params) params_kernels = params[len(self.kernels):] #compute Kd and EE Kd = np.zeros((self.n, Xtest[0].shape[0], len(self.kernels))) params_ind = 0 kernel_paramsArr = params[len(self.kernels):] for k_i, k in enumerate(self.kernels): numHyp = k.getNumParams() kernelParams_range = np.array(xrange(params_ind, params_ind+numHyp), dtype=np.int) kernel_params = kernel_paramsArr[kernelParams_range] Kd[:,:,k_i] = k.getTrainTestKernel(kernel_params, Xtest[k_i]) params_ind += numHyp EE = elsympol(Kd, len(self.kernels)) #compute K K=0 for i in xrange(len(self.kernels)): K += np.exp(2*params[i]) * EE[:,:,i+1] return K
def getTestKernelDiag(self, params, Xtest): self.checkParams(params) params_kernels = params[len(self.kernels):] #compute Kd and EE Kd = np.zeros((Xtest[0].shape[0], 1, len(self.kernels))) params_ind = 0 kernel_paramsArr = params[len(self.kernels):] for k_i, k in enumerate(self.kernels): numHyp = k.getNumParams() kernelParams_range = np.array(xrange(params_ind, params_ind+numHyp), dtype=np.int) kernel_params = kernel_paramsArr[kernelParams_range] Kd[:,0,k_i] = k.getTestKernelDiag(kernel_params, Xtest[k_i]) params_ind += numHyp EE = elsympol(Kd, len(self.kernels)) #compute K K=0 for i in xrange(len(self.kernels)): K += np.exp(2*params[i]) * EE[:,:,i+1] return K
def getTrainTestKernel(self, params, Xtest): self.checkParams(params) ell2 = np.exp(2*params[0]) z = Xtest / np.sqrt(Xtest.shape[1]) S = 1 + self.X_scaled.dot(z.T) sz = 1 + np.sum(z**2, axis=1) sqrtEll2Psx = np.sqrt(ell2+self.sx) sqrtEll2Psz = np.sqrt(ell2+sz) K = S / np.outer(sqrtEll2Psx, sqrtEll2Psz) return np.arcsin(K)
def sample_hparams(): hparams = {} for k, sample_range in ranges.items(): if isinstance(sample_range, (LogRange, LinearRange)): if isinstance(sample_range[0], int): # LogRange not valid for ints hparams[k] = random.randint(sample_range[0], sample_range[1]) elif isinstance(sample_range[0], float): start, end = sample_range if isinstance(sample_range, LogRange): start, end = np.log10(start), np.log10(end) choice = np.random.uniform(start, end) if isinstance(sample_range, LogRange): choice = np.exp(choice) hparams[k] = choice return hparams
def gaussian_kernel(size, std=1.): size2 = 1 + 2 * size kernel = np.zeros((size2, size2)) den = 2. * std * std for row in range(size2): for col in range(size2): x = row - size y = row - size kernel[row, col] = np.exp(-(x*x + y*y) / den) kernel /= kernel.sum() return kernel # TODO: check out of bounds
def EStep(self): P = np.zeros((self.M, self.N)) for i in range(0, self.M): diff = self.X - np.tile(self.TY[i, :], (self.N, 1)) diff = np.multiply(diff, diff) P[i, :] = P[i, :] + np.sum(diff, axis=1) c = (2 * np.pi * self.sigma2) ** (self.D / 2) c = c * self.w / (1 - self.w) c = c * self.M / self.N P = np.exp(-P / (2 * self.sigma2)) den = np.sum(P, axis=0) den = np.tile(den, (self.M, 1)) den[den==0] = np.finfo(float).eps self.P = np.divide(P, den) self.Pt1 = np.sum(self.P, axis=0) self.P1 = np.sum(self.P, axis=1) self.Np = np.sum(self.P1)
def create_reference_image(size, x0=10., y0=-3., sigma_x=50., sigma_y=30., dtype='float64', reverse_xaxis=False, correct_axes=True, sizey=None, **kwargs): """ Creates a reference image: a gaussian brightness with elliptical """ inc_cos = np.cos(0./180.*np.pi) delta_x = 1. x = (np.linspace(0., size - 1, size) - size / 2.) * delta_x if sizey: y = (np.linspace(0., sizey-1, sizey) - sizey/2.) * delta_x else: y = x.copy() if reverse_xaxis: xx, yy = np.meshgrid(-x, y/inc_cos) elif correct_axes: xx, yy = np.meshgrid(-x, -y/inc_cos) else: xx, yy = np.meshgrid(x, y/inc_cos) image = np.exp(-(xx-x0)**2./sigma_x - (yy-y0)**2./sigma_y) return image.astype(dtype)
def get_action_distr(self, state, beta=0.2): ''' Args: state (State) beta (float): Softmax temperature parameter. Returns: (list of floats): The i-th float corresponds to the probability mass associated with the i-th action (indexing into self.actions) ''' all_q_vals = [] for i in xrange(len(self.actions)): action = self.actions[i] all_q_vals.append(self.get_q_value(state, action)) # Softmax distribution. total = sum([numpy.exp(beta * qv) for qv in all_q_vals]) softmax = [numpy.exp(beta * qv) / total for qv in all_q_vals] return softmax
def monotoneTFosc(f): """Maps [-inf,inf] to [-inf,inf] with different constants for positive and negative part. """ if np.isscalar(f): if f > 0.: f = np.log(f) / 0.1 f = np.exp(f + 0.49 * (np.sin(f) + np.sin(0.79 * f))) ** 0.1 elif f < 0.: f = np.log(-f) / 0.1 f = -np.exp(f + 0.49 * (np.sin(0.55 * f) + np.sin(0.31 * f))) ** 0.1 return f else: f = np.asarray(f) g = f.copy() idx = (f > 0) g[idx] = np.log(f[idx]) / 0.1 g[idx] = np.exp(g[idx] + 0.49 * (np.sin(g[idx]) + np.sin(0.79 * g[idx])))**0.1 idx = (f < 0) g[idx] = np.log(-f[idx]) / 0.1 g[idx] = -np.exp(g[idx] + 0.49 * (np.sin(0.55 * g[idx]) + np.sin(0.31 * g[idx])))**0.1 return g
def make_gaussian(size, fwhm=3, center=None): """ Make a square gaussian kernel. size is the length of a side of the square fwhm is full-width-half-maximum, which can be thought of as an effective radius. """ x = np.arange(0, size, 1, float) y = x[:, np.newaxis] if center is None: x0 = y0 = size // 2 else: x0 = center[0] y0 = center[1] return np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / 2.0 / fwhm / fwhm)
def forward(self, input, *args, **kwargs): """A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. Often, sigmoid function refers to the special case of the logistic function and defined by the formula :math:`\\varphi(x) = \\frac{1}{1 + e^{-x}}` (given the input :math:`x`). Parameters ---------- input : float32 The activation (the summed, weighted input of a neuron). Returns ------- float32 in [0, 1] The output of the sigmoid function applied to the activation. """ self.last_forward = 1.0 / (1.0 + np.exp(-input)) return self.last_forward
def forward(self, input): """:math:`\\varphi(\\mathbf{x})_j = \\frac{e^{\mathbf{x}_j}}{\sum_{k=1}^K e^{\mathbf{x}_k}}` where :math:`K` is the total number of neurons in the layer. This activation function gets applied row-wise. Parameters ---------- x : float32 The activation (the summed, weighted input of a neuron). Returns ------- float32 where the sum of the row is 1 and each single value is in [0, 1] The output of the softmax function applied to the activation. """ assert np.ndim(input) == 2 self.last_forward = input x = input - np.max(input, axis=1, keepdims=True) exp_x = np.exp(x) s = exp_x / np.sum(exp_x, axis=1, keepdims=True) return s
def __call__(self, params): print '???', params sd1 = params[0] sd2 = params[1] cor = params[2] if sd1 < 0. or sd1 > 10. or sd2 < 0. or sd2 > 10. or cor < -1. or cor > 1.: return np.inf bandwidth = maths.stats.choleskysqrt2d(sd1, sd2, cor) bandwidthdet = la.det(bandwidth) bandwidthinv = la.inv(bandwidth) diff = sample[self.__iidx] - sample[self.__jidx] temp = diff.dot(bandwidthinv.T) temp *= temp e = np.exp(np.sum(temp, axis=1)) s = np.sum(e**(-.25) - 4 * e**(-.5)) cost = self.__n / bandwidthdet + (2. / bandwidthdet) * s print '!!!', cost return cost / 10000.
def step(self, mode): if mode == "train" and self.mode == "test": raise Exception("Cannot train during test mode") if mode == "train": theano_fn = self.train_fn batch_gen = self.train_batch_gen elif mode == "test": theano_fn = self.test_fn batch_gen = self.test_batch_gen else: raise Exception("Invalid mode") data = next(batch_gen) ys = data[-1] data = data[:-1] ret = theano_fn(*data) return {"prediction": np.exp(ret[0]) - 1, "answers": ys, "current_loss": ret[1], "loss_reg": ret[2], "loss_mse": ret[1] - ret[2], "log": ""}
def evaluation(self, X_test, y_test): # normalization X_test = self.normalization(X_test) # average over the output pred_y_test = np.zeros([self.M, len(y_test)]) prob = np.zeros([self.M, len(y_test)]) ''' Since we have M particles, we use a Bayesian view to calculate rmse and log-likelihood ''' for i in range(self.M): w1, b1, w2, b2, loggamma, loglambda = self.unpack_weights(self.theta[i, :]) pred_y_test[i, :] = self.nn_predict(X_test, w1, b1, w2, b2) * self.std_y_train + self.mean_y_train prob[i, :] = np.sqrt(np.exp(loggamma)) /np.sqrt(2*np.pi) * np.exp( -1 * (np.power(pred_y_test[i, :] - y_test, 2) / 2) * np.exp(loggamma) ) pred = np.mean(pred_y_test, axis=0) # evaluation svgd_rmse = np.sqrt(np.mean((pred - y_test)**2)) svgd_ll = np.mean(np.log(np.mean(prob, axis = 0))) return (svgd_rmse, svgd_ll)
def svgd_kernel(self, theta, h = -1): sq_dist = pdist(theta) pairwise_dists = squareform(sq_dist)**2 if h < 0: # if h < 0, using median trick h = np.median(pairwise_dists) h = np.sqrt(0.5 * h / np.log(theta.shape[0]+1)) # compute the rbf kernel Kxy = np.exp( -pairwise_dists / h**2 / 2) dxkxy = -np.matmul(Kxy, theta) sumkxy = np.sum(Kxy, axis=1) for i in range(theta.shape[1]): dxkxy[:, i] = dxkxy[:,i] + np.multiply(theta[:,i],sumkxy) dxkxy = dxkxy / (h**2) return (Kxy, dxkxy)
def _linear_phase(self, n_shift): """ Private: Select the center of FOV """ om = self.st['om'] M = self.st['M'] final_shifts = tuple( numpy.array(n_shift) + numpy.array(self.st['Nd']) / 2) phase = numpy.exp( 1.0j * numpy.sum( om * numpy.tile( final_shifts, (M,1)), 1)) # add-up all the linear phasees in all axes, self.st['p'] = scipy.sparse.diags(phase, 0).dot(self.st['p0'])
def nufft_scale1(N, K, alpha, beta, Nmid): ''' calculate image space scaling factor ''' # import types # if alpha is types.ComplexType: alpha = numpy.real(alpha) # print('complex alpha may not work, but I just let it as') L = len(alpha) - 1 if L > 0: sn = numpy.zeros((N, 1)) n = numpy.arange(0, N).reshape((N, 1), order='F') i_gam_n_n0 = 1j * (2 * numpy.pi / K) * (n - Nmid) * beta for l1 in range(-L, L + 1): alf = alpha[abs(l1)] if l1 < 0: alf = numpy.conj(alf) sn = sn + alf * numpy.exp(i_gam_n_n0 * l1) else: sn = numpy.dot(alpha, numpy.ones((N, 1), dtype=numpy.float32)) return sn
def linear_phase(self, n_shift): ''' Select the center of FOV ''' om = self.st['om'] M = self.st['M'] final_shifts = tuple( numpy.array(n_shift) + numpy.array(self.st['Nd']) / 2) phase = numpy.exp( 1.0j * numpy.sum( om * numpy.tile( final_shifts, (M,1)), 1)) # add-up all the linear phasees in all axes, self.st['p'] = scipy.sparse.diags(phase, 0).dot(self.st['p0']) # multiply the diagonal, linear phase before the gridding matrix
def nufft_scale1(N, K, alpha, beta, Nmid): ''' Calculate image space scaling factor ''' # import types # if alpha is types.ComplexType: alpha = numpy.real(alpha) # print('complex alpha may not work, but I just let it as') L = len(alpha) - 1 if L > 0: sn = numpy.zeros((N, 1)) n = numpy.arange(0, N).reshape((N, 1), order='F') i_gam_n_n0 = 1j * (2 * numpy.pi / K) * (n - Nmid) * beta for l1 in range(-L, L + 1): alf = alpha[abs(l1)] if l1 < 0: alf = numpy.conj(alf) sn = sn + alf * numpy.exp(i_gam_n_n0 * l1) else: sn = numpy.dot(alpha, numpy.ones((N, 1))) return sn
def f(w, lamb): """ Eq. (2) in problem 2 Non-vectorized, slow """ total = 0 nrows = X.shape[0] for i in range(nrows): current = 1 + np.exp(-y[i] * X[i, ].dot(w)) total += np.log(current) total += (lamb / 2) * w.dot(w) return total
def f2(w, lamb): """ Eq. (2) in problem 2 Vectorized (no explicit loops), fast """ yxTw = y * X.dot(w) firstpart = np.log(1 + np.exp(-yxTw)) total = firstpart.sum() total += (lamb / 2) * w.dot(w) return total
def pac_metric (solution, prediction, task='binary.classification'): ''' Probabilistic Accuracy based on log_loss metric. We assume the solution is in {0, 1} and prediction in [0, 1]. Otherwise, run normalize_array.''' debug_flag=False [sample_num, label_num] = solution.shape if label_num==1: task='binary.classification' eps = 1e-15 the_log_loss = log_loss(solution, prediction, task) # Compute the base log loss (using the prior probabilities) pos_num = 1.* sum(solution) # float conversion! frac_pos = pos_num / sample_num # prior proba of positive class the_base_log_loss = prior_log_loss(frac_pos, task) # Alternative computation of the same thing (slower) # Should always return the same thing except in the multi-label case # For which the analytic solution makes more sense if debug_flag: base_prediction = np.empty(prediction.shape) for k in range(sample_num): base_prediction[k,:] = frac_pos base_log_loss = log_loss(solution, base_prediction, task) diff = np.array(abs(the_base_log_loss-base_log_loss)) if len(diff.shape)>0: diff=max(diff) if(diff)>1e-10: print('Arrggh {} != {}'.format(the_base_log_loss,base_log_loss)) # Exponentiate to turn into an accuracy-like score. # In the multi-label case, we need to average AFTER taking the exp # because it is an NL operation pac = mvmean(np.exp(-the_log_loss)) base_pac = mvmean(np.exp(-the_base_log_loss)) # Normalize: 0 for random, 1 for perfect score = (pac - base_pac) / sp.maximum(eps, (1 - base_pac)) return score
def softmax(x): e_x = np.exp(x - np.max(x)) out = e_x / e_x.sum() return out
def f(r,theta): out = np.sin(theta)*r*np.exp(-r/2.) out[-1] = 0 return out
def dfdr(r,theta): out = np.sin(theta)*(np.exp(-r/2.) - (1./2.)*r*np.exp(-r/2.)) out[-1] = 0 return out
def dfdrdtheta(r,theta): out = np.cos(theta)*(np.exp(-r/2.) - (1./2.)*r*np.exp(-r/2.)) out[-1] = 0 return out
def sample(self, probs, temperature): if temperature == 0: return np.argmax(probs) probs = probs.astype(np.float64) #convert to float64 for higher precision probs = np.log(probs) / temperature probs = np.exp(probs) / math.fsum(np.exp(probs)) return np.argmax(np.random.multinomial(1, probs, 1)) #generate a sentence given conv_hidden
def softmax(x): act = np.exp(x - np.max(x)) return act / act.sum()
