deficient | laravel components , an easy way

Related to this question: How to have negative zero always formatted as positive zero in a python string?

I have the following function that implements Matlab's orth.m using Numpy. I have a docstring test that relies on np.array2string using suppress_small=True, which will make small values round to zero. However, sometimes they round to positive zero and sometimes they round to negative zero, depending on whether the answer comes out as 1e-16 or -1e-17 or similar. Which case happens is based on the SVD decomposition, and can vary from platform to platform or across Python versions depending on which underlying linear algebra solver is used (BLAS, Lapack, etc.)

What's the best way to design the docstring test to account for this?

In the final doctest, sometimes the Q[0, 1] term is -0. and sometimes it's 0.

I want to create something that looks like below.

Few bordered boxes with equal vertical spacing.

After a bit of research, this is the best I can come up with.

I want to simulate the predicted value from a linear regression model 1000 times and see how many of those times each type of car has the highest predicted mpg base on the independent variables of the model. I use the test and training set because I want to evaluate the fit of the model outside the training data.

I have the following code:

I'm studying inverse problem, and I tried to inverse the matrix A, but it has 'rank deficient'. The matrix A is non-square matrix too. So, I tried b times pinv(A), but it yielded not correct solution.

I want to know the method or function that can solve this problem.

This is the MATLAB code

I have a component which renders based on data which is transformed from the properties. Specifically, one of the properties for the component is straight out JSON. That JSON is then transformed by the component into the actual data used in the rendering.

So this is the bit I dont get. If I updated my props then the render function gets called before I have the opportunity to transform into the data for rendering. I discovered the componentWillReceiveProps function which works perfectly, I can prepare my data prior to the rendering.

However I am seeing that this is now being deprecated and the other suggestions seem to be deficient in their own ways (either called after render, or not called on mount etc)

I dont think my use case seems particularly unique, or maybe seeing as I am knew to React I am misunderstaning the usage of props, but if not then why does this not seem to be supported?

What options do I have to update my component with new data and have that data transformed prior to the render occuring?

For clarity this is what i am doing. And the props.data is the property that is transformed by the component ideally prior to rendering.

I’m trying to come up with a solution for a specific time series analysis in PL/SQL. I’ll use a very simple data set to explain the scenario. In the COMP_TEST table an event gets a row created for it as long as there are individual calendar days (CAL_DATE) between the EVENT_START_DATE and EVENT_END_DATE. In case of EVENT_ID 1772349 we get rows for 10/01/2020 – 10/24/2020 since that is the start and end range. On each given calendar day, the results of some event are recorded as ‘yes’ or ‘no’ in the EVENT_RESULT column.

Requirements to check:

• There should be a ‘yes’ EVENT_RESULT every 7 calendar days.
• There should not be more than 7 calendar days between two ‘yes’ results.
• A consecutive 7 calendar day block of ‘no’ results will get counted as one unit of non-compliance/deficiency.
• Every time a ‘yes’ result is encountered, the count for evaluating the next 7 calendar days for the ‘no’ results should reset back to 1.
• If a ‘no’ result is encountered on the CAL_DATE that is equal to the EVENT_END_DATE then that will count as one unit of non-compliance/deficiency as well

NOTE: 7 calendar days here should be start and end dates inclusive.

In the case of EVENT_ID 1772349 the first CAL_DATE 01-OCT-20 had a ‘yes’ result so the next 7 calendar day evaluation would start at 02-OCT-20 (day 1) and end at 08-OCT-20 (day 7) where the ‘yes’ result was recorded. The second evaluation would start with 09-OCT-20 which happens to be a ‘yes’ result also so the next count would actually begin with 10-OCT-20 where the result was a ‘no’ and continued to be ‘no’ till 16-OCT-20 for 7 consecutive days. Then the third evaluation would begin with 17-OCT-20 after which there was only one additional ‘no’ result on 18-OCT-20 and then all days 19-OCT-20 to 23-OCT-20 were ‘yes’ results. Finally on the last calendar day of the event 24-OCT-20 we hit a ‘no’ which would get classified as a deficiency.

I have included the create table script, query that I’ve attempted, and expected outputs below. Since this sounded to fit the gaps and islands problem I started with that approach but didn’t know how to tackle the rest of the requirements.

TABLE CREATE SCRIPT:

I'm conducting a multivariate multiple regression. As response variable, I have a function [ y(t) ] that I have discretized on a grid of 27 points, and 3 scalar regressors (x1,x2,x3). I have replaced the response function with an nxq (q=27) matrix Y and what I need to solve is:

Y = XB + E

where X [nxp (p=3)] is x1,x2,x3 column-stacked, B is a p×q matrix of regression coefficients and E is an n×q matrix of errors. What I have done up to now is calling lm: mylm<-lm(Y ~ X) which regresses each dependent variable separately on the predictors.

Now I want to determine whether a predictor jointly contributes to all the 27 models I get, but I don't know how to overcome the errors I get. When I call Anova this is what I get

I am using ipyvutify trying to code great GUIs. It looks fantastic but a bit hard for someone who had never did HTML & CSS.

The following is a complete code that generates a dialog floating when clicking "OPEN DIALOG" button.

I would like to unterstand the functioning of opening and closing floating dialogs using ipyvuetify and with the following code I can not figure it out even if the code works (deficiently).

The GOAL is to open a dialog with a button and having a CLOSE button inside the dialog as well (together with other buttons)