我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用scipy.amax()。
def cvcompute(mat, weights, foldid, nlams): if len(weights.shape) > 1: weights = scipy.reshape(weights, [weights.shape[0], ]) wisum = scipy.bincount(foldid, weights = weights) nfolds = scipy.amax(foldid) + 1 outmat = scipy.ones([nfolds, mat.shape[1]])*scipy.NaN good = scipy.zeros([nfolds, mat.shape[1]]) mat[scipy.isinf(mat)] = scipy.NaN for i in range(nfolds): tf = foldid == i mati = mat[tf, ] wi = weights[tf, ] outmat[i, :] = wtmean(mati, wi) good[i, 0:nlams[i]] = 1 N = scipy.sum(good, axis = 0) cvcpt = dict() cvcpt['cvraw'] = outmat cvcpt['weights'] = wisum cvcpt['N'] = N return(cvcpt) # end of cvcompute #=========================
def lambda_interp(lambdau, s): # lambda is the index sequence that is produced by the model # s is the new vector at which evaluations are required. # the value is a vector of left and right indices, and a vector of fractions. # the new values are interpolated bewteen the two using the fraction # Note: lambda decreases. you take: # sfrac*left+(1-sfrac*right) if len(lambdau) == 1: nums = len(s) left = scipy.zeros([nums, 1], dtype = scipy.integer) right = left sfrac = scipy.zeros([nums, 1], dtype = scipy.float64) else: s[s > scipy.amax(lambdau)] = scipy.amax(lambdau) s[s < scipy.amin(lambdau)] = scipy.amin(lambdau) k = len(lambdau) sfrac = (lambdau[0] - s)/(lambdau[0] - lambdau[k - 1]) lambdau = (lambdau[0] - lambdau)/(lambdau[0] - lambdau[k - 1]) coord = scipy.interpolate.interp1d(lambdau, range(k))(sfrac) left = scipy.floor(coord).astype(scipy.integer, copy = False) right = scipy.ceil(coord).astype(scipy.integer, copy = False) # tf = left != right sfrac[tf] = (sfrac[tf] - lambdau[right[tf]])/(lambdau[left[tf]] - lambdau[right[tf]]) sfrac[~tf] = 1.0 #if left != right: # sfrac = (sfrac - lambdau[right])/(lambdau[left] - lambdau[right]) #else: # sfrac[left == right] = 1.0 result = dict() result['left'] = left result['right'] = right result['frac'] = sfrac return(result) # end of lambda_interp # =========================================
def __MR_W_D_matrix(self,img,labels): s = sp.amax(labels)+1 vect = self.__MR_superpixel_mean_vector(img,labels) adj = self.__MR_get_adj_loop(labels) W = sp.spatial.distance.squareform(sp.spatial.distance.pdist(vect)) W = sp.exp(-1*W / self.weight_parameters['delta']) W[adj.astype(np.bool)] = 0 D = sp.zeros((s,s)).astype(float) for i in range(s): D[i, i] = sp.sum(W[i]) return W,D
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 __MR_superpixel_mean_vector(self,img,labels): s = sp.amax(labels)+1 vec = sp.zeros((s,3)).astype(float) for i in range(s): mask = labels == i super_v = img[mask].astype(float) mean = sp.mean(super_v,0) vec[i] = mean return vec
def __MR_affinity_matrix(self,img,labels): W,D = self.__MR_W_D_matrix(img,labels) aff = pinv(D-self.weight_parameters['alpha']*W) aff[sp.eye(sp.amax(labels)+1).astype(bool)] = 0.0 # diagonal elements to 0 return aff
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_fill_superpixel_with_saliency(self,labels,saliency_score): sa_img = labels.copy().astype(float) for i in range(sp.amax(labels)+1): mask = labels == i sa_img[mask] = saliency_score[i] return cv2.normalize(sa_img,None,0,255,cv2.NORM_MINMAX)
