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.vstack()。
def __getitem__(self, item): # TODO should be right for all the common use... But better write down a TestCase if hasattr(self, 'process_all') and self.process_all: # keep attr check! return self.data[item] if isinstance(item, int): return self.get_context(item=item) if isinstance(item, tuple): if len(item) == 2: rows, columns = item if isinstance(rows, int) and isinstance(columns, int): # TODO check here # do you want the particular element? return self.get_context(item=rows)[columns] else: raise TypeError('NOT IMPLEMENTED <|>') if isinstance(rows, slice): rows = range(*rows.indices(self.shape[0])) return np.vstack([self.get_context(r) for r in rows])[:, columns] else: if isinstance(item, slice): item = range(*item.indices(self.shape[0])) return np.vstack([self.get_context(r) for r in item])
def stack(*datasets): """ Assuming that the datasets have same structure, stucks data and targets :param datasets: :return: stacked dataset """ return Dataset(data=vstack([d.data for d in datasets]), target=stack_or_concat([d.target for d in datasets]), sample_info=stack_or_concat([d.sample_info for d in datasets]), info={k: [d.info.get(k, None) for d in datasets] for k in merge_dicts(*[d.info for d in datasets])})
def stack_or_concat(list_of_arays): func = np.concatenate if list_of_arays[0].ndim == 1 else np.vstack return func(list_of_arays)
def vstack(lst): """ Vstack that considers sparse matrices :param lst: :return: """ return sp.vstack(lst) if sp and isinstance(lst[0], sp.sparse.csr.csr_matrix) else np.vstack(lst)
def auc_mat(y, prob, weights = None): if weights == None or len(weights) == 0: weights = scipy.ones([y.shape[0], 1]) wweights = weights*y wweights = wweights.flatten() wweights = scipy.reshape(wweights, [1, wweights.size]) ny= y.shape[0] a = scipy.zeros([ny, 1]) b = scipy.ones([ny, 1]) yy = scipy.vstack((a, b)) pprob = scipy.vstack((prob,prob)) result = auc(yy, pprob, wweights) return(result) #=========================
def get_train_data(n_pos = 46443, n_neg = 206940,k=12): ''' megre positive and negative examples ''' suff = str(k) X_name = 'train_data_'+ suff + '.npz' y_name = 'labels_'+ suff + '.npz' if not(os.path.exists(X_name) and os.path.exists(y_name)): X_pos = [] # X_train_face,y_train_face = Datasets.get_train_face_wider_data(k = k) # X_pos = X_train_face[y_train_face==1] # X_pos = X_train_face X_aflw,y_train_face_aflw = Datasets.get_aflw_face_data(k = k) # if len(X_pos) > 0: # X_pos = sp.vstack( [X_pos,X_aflw] ) # else: # X_pos = X_aflw X_pos = X_aflw X_train_non_face,y_train_non_face = Datasets.get_train_non_face_data(k = k) print('c1_pos:',len(X_pos)) #print((X_train_face[y_train_face==0].shape,X_train_non_face.shape)) # if len(X_train_face[y_train_face==0]) > 0: # X_neg = sp.vstack( (X_train_face[y_train_face==0],X_train_non_face) ) # else: # X_neg = X_train_non_face X_neg = X_train_non_face X_pos = shuffle(X_pos,random_state=42) X_neg = shuffle(X_neg,random_state=42) X_pos = X_pos[:n_pos] X_neg = X_neg[:n_neg] n_neg = len(X_neg) n_pos = len(X_pos) y_pos = sp.ones(n_pos,int) y_neg = sp.zeros(n_neg,int) X = sp.vstack((X_pos,X_neg)) y = sp.hstack( (y_pos,y_neg) ) X,y = shuffle(X,y,random_state=42) sp.savez(X_name,X) sp.savez(y_name,y)
def qzdiv(stake, A, B, Q, Z): ''' Takes U.T. matrices A, B, orthonormal matrices Q,Z, rearranges them so that all cases of abs(B(i,i)/A(i,i))>stake are in lower right corner, while preserving U.T. and orthonormal properties and Q'AZ' and Q'BZ'. Parameters ---------- stake : number, dtype=float A : array_like, dtype=float An upper triangular matrix B : array_like, dtype=float An upper triangular matrix Q : array_like, dtype=float An orthonormal matrix from the QZ decomposition Z : array_like, dtype=float An orthonormal matrix from the QZ decomposition Returns ------- A : array_like, dtype=float Rearranged A matrix B : array_like, dtype=float Rearranged B matrix Q : array_like, dtype=float Rearranged Q matrix Z : array_like, dtype=float Rearranged Z matrix Notes ----- Copyright: C.A. Sims, 1996, Yale University. ''' n, jnk = A.shape root = abs(vstack((np.diag(A), np.diag(B))).T) tmp = (root[:,0]<1.e-13).astype(int) root[:,0] = root[:,0]- tmp *(root[:,0]+root[:,1]) root[:,1] = root[:,1]/root[:,0] for i in xrange(n,0,-1): m=0 for j in xrange(i,0,-1): if (root[j-1,1] > stake or root[j-1,1] < -.1): m=j break if m==0: print "qzdiv(): Inputs unchanged!" return A, B, Q, Z for k in xrange(m,i,1): A, B, Q, Z = qzswitch(k,A,B,Q,Z) tmp = root[k-1,1] root[k-1,1] = root[k,1] root[k,1] = tmp return A, B, Q, Z
def qzdiv(stake, A, B, Q, Z): ''' Takes U.T. matrices A, B, orthonormal matrices Q,Z, rearranges them so that all cases of abs(B(i,i)/A(i,i))>stake are in lower right corner, while preserving U.T. and orthonormal properties and Q'AZ' and Q'BZ'. Parameters ---------- stake : number, dtype=float A : array_like, dtype=float An upper triangular matrix B : array_like, dtype=float An upper triangular matrix Q : array_like, dtype=float An orthonormal matrix from the QZ decomposition Z : array_like, dtype=float An orthonormal matrix from the QZ decomposition Returns ------- A : array_like, dtype=float Rearranged A matrix B : array_like, dtype=float Rearranged B matrix Q : array_like, dtype=float Rearranged Q matrix Z : array_like, dtype=float Rearranged Z matrix Notes ----- Copyright: C.A. Sims, 1996, Yale University. ''' n, jnk = A.shape root = abs(vstack((np.diag(A), np.diag(B))).T) tmp = (root[:,0]<1.e-13).astype(int) root[:,0] = root[:,0]- tmp *(root[:,0]+root[:,1]) root[:,1] = root[:,1]/root[:,0] for i in range(n,0,-1): m=0 for j in range(i,0,-1): if (root[j-1,1] > stake or root[j-1,1] < -.1): m=j break if m==0: print ("qzdiv(): Inputs unchanged!") return A, B, Q, Z for k in range(m,i,1): A, B, Q, Z = qzswitch(k,A,B,Q,Z) tmp = root[k-1,1] root[k-1,1] = root[k,1] root[k,1] = tmp return A, B, Q, Z
