我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用scipy.unique()。
def auc(y, prob, w): if len(w) == 0: mindiff = scipy.amin(scipy.diff(scipy.unique(prob))) pert = scipy.random.uniform(0, mindiff/3, prob.size) t, rprob = scipy.unique(prob + pert, return_inverse = True) n1 = scipy.sum(y, keepdims = True) n0 = y.shape[0] - n1 u = scipy.sum(rprob[y == 1]) - n1*(n1 + 1)/2 result = u/(n1*n0) else: op = scipy.argsort(prob) y = y[op] w = w[op] cw = scipy.cumsum(w) w1 = w[y == 1] cw1 = scipy.cumsum(w1) wauc = scipy.sum(w1*(cw[y == 1] - cw1)) sumw = cw1[-1] sumw = sumw*(c1[-1] - sumw) result = wauc/sumw return(result) #=========================
def pred_accuracy(y_true, y_pred): y_true = sp.copy(y_true) if len(sp.unique(y_true))==2: print 'dichotomous trait, calculating AUC' y_min = y_true.min() y_max = y_true.max() if y_min!= 0 or y_max!=1: y_true[y_true==y_min]=0 y_true[y_true==y_max]=1 fpr, tpr, thresholds = metrics.roc_curve(y_true, y_pred) auc = metrics.auc(fpr, tpr) return auc else: print 'continuous trait, calculating COR' cor = sp.corrcoef(y_true,y_pred)[0,1] return cor
def __MR_boundary_indictor(self,labels): s = sp.amax(labels)+1 up_indictor = (sp.zeros((s,1))).astype(float) right_indictor = (sp.zeros((s,1))).astype(float) low_indictor = (sp.zeros((s,1))).astype(float) left_indictor = (sp.zeros((s,1))).astype(float) upper_ids = sp.unique(labels[0,:]).astype(int) right_ids = sp.unique(labels[:,labels.shape[1]-1]).astype(int) low_ids = sp.unique(labels[labels.shape[0]-1,:]).astype(int) left_ids = sp.unique(labels[:,0]).astype(int) up_indictor[upper_ids] = 1.0 right_indictor[right_ids] = 1.0 low_indictor[low_ids] = 1.0 left_indictor[left_ids] = 1.0 return up_indictor,right_indictor,low_indictor,left_indictor
def learn(self,x,y): ''' Function that learns the GMM with ridge regularizationb from training samples Input: x : the training samples y : the labels Output: the mean, covariance and proportion of each class, as well as the spectral decomposition of the covariance matrix ''' ## Get information from the data C = sp.unique(y).shape[0] #C = int(y.max(0)) # Number of classes n = x.shape[0] # Number of samples d = x.shape[1] # Number of variables eps = sp.finfo(sp.float64).eps ## Initialization self.ni = sp.empty((C,1)) # Vector of number of samples for each class self.prop = sp.empty((C,1)) # Vector of proportion self.mean = sp.empty((C,d)) # Vector of means self.cov = sp.empty((C,d,d)) # Matrix of covariance self.Q = sp.empty((C,d,d)) # Matrix of eigenvectors self.L = sp.empty((C,d)) # Vector of eigenvalues self.classnum = sp.empty(C).astype('uint8') ## Learn the parameter of the model for each class for c,cR in enumerate(sp.unique(y)): j = sp.where(y==(cR))[0] self.classnum[c] = cR # Save the right label self.ni[c] = float(j.size) self.prop[c] = self.ni[c]/n self.mean[c,:] = sp.mean(x[j,:],axis=0) self.cov[c,:,:] = sp.cov(x[j,:],bias=1,rowvar=0) # Normalize by ni to be consistent with the update formulae # Spectral decomposition L,Q = linalg.eigh(self.cov[c,:,:]) idx = L.argsort()[::-1] self.L[c,:] = L[idx] self.Q[c,:,:]=Q[:,idx]
def __MR_second_stage_indictor(self,saliency_img_mask,labels): s = sp.amax(labels)+1 # get ids from labels image ids = sp.unique(labels[saliency_img_mask]).astype(int) # indictor indictor = sp.zeros((s,1)).astype(float) indictor[ids] = 1.0 return indictor
def __MR_get_adj_loop(self, labels): s = sp.amax(labels) + 1 adj = np.ones((s, s), np.bool) for i in range(labels.shape[0] - 1): for j in range(labels.shape[1] - 1): if labels[i, j]<>labels[i+1, j]: adj[labels[i, j], labels[i+1, j]] = False adj[labels[i+1, j], labels[i, j]] = False if labels[i, j]<>labels[i, j + 1]: adj[labels[i, j], labels[i, j+1]] = False adj[labels[i, j+1], labels[i, j]] = False if labels[i, j]<>labels[i + 1, j + 1]: adj[labels[i, j] , labels[i+1, j+1]] = False adj[labels[i+1, j+1], labels[i, j]] = False if labels[i + 1, j]<>labels[i, j + 1]: adj[labels[i+1, j], labels[i, j+1]] = False adj[labels[i, j+1], labels[i+1, j]] = False upper_ids = sp.unique(labels[0,:]).astype(int) right_ids = sp.unique(labels[:,labels.shape[1]-1]).astype(int) low_ids = sp.unique(labels[labels.shape[0]-1,:]).astype(int) left_ids = sp.unique(labels[:,0]).astype(int) bd = np.append(upper_ids, right_ids) bd = np.append(bd, low_ids) bd = sp.unique(np.append(bd, left_ids)) for i in range(len(bd)): for j in range(i + 1, len(bd)): adj[bd[i], bd[j]] = False adj[bd[j], bd[i]] = False return adj
