EMS | Advanced Education System using Tensorflow | Machine Learning library

Failed to construct transformer: Error: error:0308010C:digital envelope routines::unsupported

The simplest and easiest solution to solve the above error is to downgrade Node.js to v14.18.1. And then just delete folder node_modules and try to rebuild your project and your error must be solved.

In addition to the provided comments:

%$% also called the Exposition pipe vs. %>%:

This is a short summary of this article https://towardsdatascience.com/3-lesser-known-pipe-operators-in-tidyverse-111d3411803a

"The key difference in using %$% or %>% lies in the type of arguments of used functions."

One advantage, and as far as I can understand it, for me the only one to use %$% over %>% is the fact that we can avoid repetitive input of the dataframe name in functions that have no data as an argument.

For example the lm() has a data argument. In this case we can use both %>% and %$% interchangeable.

But in functions like the cor() which has no data argument:

It looks like GCC's naïveté about store-forwarding stalls is hurting its auto-vectorization strategy here. See also Store forwarding by example for some practical benchmarks on Intel with hardware performance counters, and What are the costs of failed store-to-load forwarding on x86? Also Agner Fog's x86 optimization guides.

(gcc -O3 enables -ftree-vectorize and a few other options not included by -O2, e.g. if-conversion to branchless cmov, which is another way -O3 can hurt with data patterns GCC didn't expect. By comparison, Clang enables auto-vectorization even at -O2, although some of its optimizations are still only on at -O3.)

It's doing 64-bit loads (and branching to store or not) on pairs of ints. This means, if we swapped the last iteration, this load comes half from that store, half from fresh memory, so we get a store-forwarding stall after every swap. But bubble sort often has long chains of swapping every iteration as an element bubbles far, so this is really bad.

(Bubble sort is bad in general, especially if implemented naively without keeping the previous iteration's second element around in a register. It can be interesting to analyze the asm details of exactly why it sucks, so it is fair enough for wanting to try.)

Anyway, this is pretty clearly an anti-optimization you should report on GCC Bugzilla with the "missed-optimization" keyword. Scalar loads are cheap, and store-forwarding stalls are costly. (Can modern x86 implementations store-forward from more than one prior store? no, nor can microarchitectures other than in-order Atom efficiently load when it partially overlaps with one previous store, and partially from data that has to come from the L1d cache.)

Even better would be to keep buf[x+1] in a register and use it as buf[x] in the next iteration, avoiding a store and load. (Like good hand-written asm bubble sort examples, a few of which exist on Stack Overflow.)

If it wasn't for the store-forwarding stalls (which AFAIK GCC doesn't know about in its cost model), this strategy might be about break-even. SSE 4.1 for a branchless pmind / pmaxd comparator might be interesting, but that would mean always storing and the C source doesn't do that.

If this strategy of double-width load had any merit, it would be better implemented with pure integer on a 64-bit machine like x86-64, where you can operate on just the low 32 bits with garbage (or valuable data) in the upper half. E.g.,

Most of the solutions here would be used with melt, so to know the method melt, see the documentaion explanation

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

And the parameters are:

Parameters

• id_vars : tuple, list, or ndarray, optional

  Column(s) to use as identifier variables.

• value_vars : tuple, list, or ndarray, optional

  Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

• var_name : scalar

  Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.

• value_name : scalar, default ‘value’

  Name to use for the ‘value’ column.

• col_level : int or str, optional

  If columns are a MultiIndex then use this level to melt.

• ignore_index : bool, default True

  If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

  New in version 1.1.0.

Melting merges multiple columns and converts the dataframe from wide to long, for the solution to Problem 1 (see below), the steps are:

1. First we got the original dataframe.

2. Then the melt firstly merges the Math and English columns and makes the dataframe replicated (longer).

3. Then finally adds the column Subject which is the subject of the Grades columns value respectively.

This is the simple logic to what the melt function does.

I will solve my own questions.

Problem 1 could be solve using pd.DataFrame.melt with the following code:

I was facing a similar issue with DevOps build agents. But I can access the DevOps server web interface without any issue.

To solve this,

• I updated my Let's Encrypt client (I'm using Certify The Web)
• I have renewed my certificate

After that, the DevOps agent is able to do a Git pull.

How about something like:

I know you tried it with

From this blog post by Eric Niebler, whose range-V3 library heavily influenced C++20 ranges

... custom view types can inher[i]t from view_interface to get a host of useful member functions, like .front(), .back(), .empty(), .size(), .operator[], and even an explicit conversion to bool so that view types can be used in if statements

It seems a bug of Docker Development Environments Preview. The IntelliJ IDEA is opened by this process.