constraints

问题 I use this code: func show(){ view.translatesAutoresizingMaskIntoConstraints = false view.leftAnchor.constraint(equalTo: view.leftAnchor, constant: 0).isActive = true view.topAnchor.constraint(equalTo: view.topAnchor, constant: 0).isActive = true view.heightAnchor.constraint(equalToConstant: view.frame.height - 300).isActive = true view.rightAnchor.constraint(equalTo: view.rightAnchor, constant: 0).isActive = true } func hide(){ view.translatesAutoresizingMaskIntoConstraints = false view

问题 So we're looking for a way to enforce constraints that span multiple tables. We've come across this old blog post, which suggests: Create a materialized view to select data that violates the desired constraint. The MV must be defined with REFRESH COMPLETE ON COMMIT so that it is updated before the end of the transaction. Create a check constraint on the materialized view that always evaluates to FALSE – e.g. CHECK (1=0) That’s it. Whenever the underlying tables are updated, the materialized

问题 So we're looking for a way to enforce constraints that span multiple tables. We've come across this old blog post, which suggests: Create a materialized view to select data that violates the desired constraint. The MV must be defined with REFRESH COMPLETE ON COMMIT so that it is updated before the end of the transaction. Create a check constraint on the materialized view that always evaluates to FALSE – e.g. CHECK (1=0) That’s it. Whenever the underlying tables are updated, the materialized

问题 I am adding constraints to a neo4j database using Cypher constraints and want to create a constraint which only applies to a subset of a node type. I can create a constraint that a foo field must exist on Entity with CREATE CONSTRAINT ON (e:Entity) ASSERT EXISTS (e.foo) but instead, I want to constrain only nodes with a given field. e.g CREATE CONSTRAINT ON (e:Entity {constrain_flag:true) ASSERT EXISTS (e.foo) For example, I may have two nodes like (e:Entity { foo: 'bar' , constrain_flag:

问题 I have MS Access database file with following tables Table In Id | Quantity ------------------------ 1 | 8 Table Out Id | Parent | Quantity ------------------------- 1 | 1 | 2 2 | 1 | 5 Table In is a parent table and Table Out is related table. I need following constraint to be enforced (pseudo formula): In.Quantity >= Sum(Out.Quantity) That is: Sum of outgoing quantities cannot be greater than incoming quantity I am developing a Winforms application. The user will make changes (Insert,

问题 I am using SCIPY to optimize a storage facility that uses forward prices for a deal term of 1 year. Gas can be injected and withdrawn from this facility, based on monthly spreads (e.g. March 21 vs May 20 spread) being high enough to cover the variable cost of operation. The attached picture represents the problem (the values here are arbitrary, don't match the values in the code; pic is just for concept) The cells in blue are the "changing cells", volumes that SCIPY will adjust to maximize

问题 I am trying to fit a quadratic to some experimental data and using polyfit in numpy. I am looking to get a concave curve, and hence want to make sure that the coefficient of the quadratic term is negative, also the fit itself is weighted, as in there are some weights on the points. Is there an easy way to do that? Thanks. 回答1: The use of weights is described here (numpy.polyfit). Basically, you need a weight vector with the same length as x and y. To avoid the wrong sign in the coefficient,

问题 I am trying to fit a quadratic to some experimental data and using polyfit in numpy. I am looking to get a concave curve, and hence want to make sure that the coefficient of the quadratic term is negative, also the fit itself is weighted, as in there are some weights on the points. Is there an easy way to do that? Thanks. 回答1: The use of weights is described here (numpy.polyfit). Basically, you need a weight vector with the same length as x and y. To avoid the wrong sign in the coefficient,

问题 I am trying to fit a quadratic to some experimental data and using polyfit in numpy. I am looking to get a concave curve, and hence want to make sure that the coefficient of the quadratic term is negative, also the fit itself is weighted, as in there are some weights on the points. Is there an easy way to do that? Thanks. 回答1: The use of weights is described here (numpy.polyfit). Basically, you need a weight vector with the same length as x and y. To avoid the wrong sign in the coefficient,

问题 Here is the spline without constraints: from geomdl import fitting from geomdl.visualization import VisMPL path = [(2077.0, 712.0, 1136.6176470588234), (2077.0004154771536, 974.630482962754, 1313.735294117647), (2077.1630960823995, 1302.460574562254, 1490.8529411764707), (2078.1944091179635, 1674.693193015173, 1667.9705882352941), (2080.5096120056783, 2086.976611915444, 1845.0882352941176), (2085.1051468332066, 2711.054258877495, 2022.2058823529412), (1477.0846185328733, 2803.6223679691457,