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t9 | predictive text input system | Machine Learning library

 by   roma98 Java Version: Current License: No License

 by   roma98 Java Version: Current License: No License

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kandi X-RAY | t9 Summary

t9 is a Java library typically used in Artificial Intelligence, Machine Learning applications. t9 has no bugs, it has no vulnerabilities and it has high support. However t9 build file is not available. You can download it from GitHub.
Implementation in java of a predictive text input system (as used by cellphones using a numeric keypad) The number to letter mapping to use is the standard phone keypad number.
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Quality
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Security
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kandi-support Support

  • t9 has a highly active ecosystem.
  • It has 37 star(s) with 0 fork(s). There are no watchers for this library.
  • It had no major release in the last 12 months.
  • t9 has no issues reported. There are no pull requests.
  • It has a positive sentiment in the developer community.
  • The latest version of t9 is current.
t9 Support
Best in #Machine Learning
Average in #Machine Learning
t9 Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • t9 has no bugs reported.
t9 Quality
Best in #Machine Learning
Average in #Machine Learning
t9 Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • t9 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
t9 Security
Best in #Machine Learning
Average in #Machine Learning
t9 Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • t9 does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
t9 License
Best in #Machine Learning
Average in #Machine Learning
t9 License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • t9 releases are not available. You will need to build from source code and install.
  • t9 has no build file. You will be need to create the build yourself to build the component from source.
t9 Reuse
Best in #Machine Learning
Average in #Machine Learning
t9 Reuse
Best in #Machine Learning
Average in #Machine Learning
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t9 Key Features

t9 prediction

Print line that meets conditions in different lines with awk

copy iconCopydownload iconDownload
awk '$2=="t1"{ if(prev2!="" && prev!="") print prev2 }
     { prev=($3=="X"?prev2:""); prev2=$0 }' input
-----------------------
$ awk '$2=="t1" && third=="X"{print line2}
       {line2=line1; line1=$0; third=$3}' ip.txt
w171930 t1 Y z2545377
w171931 t1 Y z2555698
w171932 t1 Y z2554345
w171933 t8 Y z6334512
-----------------------
Search on $3 == "X" and print the previous line if the next line has a $2 == "t1"
If $2 == "t1" in the current line and $3 == "X" in the previous line then print the line before that.
$ awk '($2=="t1") && (p3=="X") {print pp0} {pp0=p0; p0=$0; p3=$3}' file
w171930 t1 Y z2545377 <--- print this line
w171931 t1 Y z2555698 <--- print this line
w171932 t1 Y z2554345 <--- print this line
w171933 t8 Y z6334512 <--- print this line
-----------------------
Search on $3 == "X" and print the previous line if the next line has a $2 == "t1"
If $2 == "t1" in the current line and $3 == "X" in the previous line then print the line before that.
$ awk '($2=="t1") && (p3=="X") {print pp0} {pp0=p0; p0=$0; p3=$3}' file
w171930 t1 Y z2545377 <--- print this line
w171931 t1 Y z2555698 <--- print this line
w171932 t1 Y z2554345 <--- print this line
w171933 t8 Y z6334512 <--- print this line
-----------------------
Search on $3 == "X" and print the previous line if the next line has a $2 == "t1"
If $2 == "t1" in the current line and $3 == "X" in the previous line then print the line before that.
$ awk '($2=="t1") && (p3=="X") {print pp0} {pp0=p0; p0=$0; p3=$3}' file
w171930 t1 Y z2545377 <--- print this line
w171931 t1 Y z2555698 <--- print this line
w171932 t1 Y z2554345 <--- print this line
w171933 t8 Y z6334512 <--- print this line
-----------------------
perl -0777 -nE 'while (/^.*\R(?=(?:\S+\s){2}X\s\S+\R\S+\st1)/gm) {print $&}' file
grep -ozP '.*\R(?=(?:\S+\s){2}X\s\S+\R\S+\st1)' file | tr -d '\000'
ruby -e 'puts $<.read.scan(/(.*\R)(?=(?:\S+\s){2}X\s\S+\R\S+\st1)/)' file
w171930 t1 Y z2545377 <--- print this line
w171931 t1 Y z2555698 <--- print this line
w171932 t1 Y z2554345 <--- print this line
w171933 t8 Y z6334512 <--- print this line
-----------------------
perl -0777 -nE 'while (/^.*\R(?=(?:\S+\s){2}X\s\S+\R\S+\st1)/gm) {print $&}' file
grep -ozP '.*\R(?=(?:\S+\s){2}X\s\S+\R\S+\st1)' file | tr -d '\000'
ruby -e 'puts $<.read.scan(/(.*\R)(?=(?:\S+\s){2}X\s\S+\R\S+\st1)/)' file
w171930 t1 Y z2545377 <--- print this line
w171931 t1 Y z2555698 <--- print this line
w171932 t1 Y z2554345 <--- print this line
w171933 t8 Y z6334512 <--- print this line
-----------------------
perl -0777 -nE 'while (/^.*\R(?=(?:\S+\s){2}X\s\S+\R\S+\st1)/gm) {print $&}' file
grep -ozP '.*\R(?=(?:\S+\s){2}X\s\S+\R\S+\st1)' file | tr -d '\000'
ruby -e 'puts $<.read.scan(/(.*\R)(?=(?:\S+\s){2}X\s\S+\R\S+\st1)/)' file
w171930 t1 Y z2545377 <--- print this line
w171931 t1 Y z2555698 <--- print this line
w171932 t1 Y z2554345 <--- print this line
w171933 t8 Y z6334512 <--- print this line
-----------------------
perl -0777 -nE 'while (/^.*\R(?=(?:\S+\s){2}X\s\S+\R\S+\st1)/gm) {print $&}' file
grep -ozP '.*\R(?=(?:\S+\s){2}X\s\S+\R\S+\st1)' file | tr -d '\000'
ruby -e 'puts $<.read.scan(/(.*\R)(?=(?:\S+\s){2}X\s\S+\R\S+\st1)/)' file
w171930 t1 Y z2545377 <--- print this line
w171931 t1 Y z2555698 <--- print this line
w171932 t1 Y z2554345 <--- print this line
w171933 t8 Y z6334512 <--- print this line
-----------------------
<<<"${aa}" mawk '

 BEGIN {
       _+=++_
 } {
     do {____=$((__=$_)<"") 

     } while($(_+getline)=="Y")
   
     if(___<("X"==$++_)) {

       printf("%s%.*s",____,\
           (___=__!="t1")<_,ORS) }; -—_}'

w171930 t1 Y z2545377
w171931 t1 Y z2555698
w171932 t1 Y z2554345
w171933 t8 Y z6334512

Create subset of data frame to indicate limits in geom_ribbon

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ggplot(high, aes(Time, mean))+
geom_line( aes( linetype=Trat, color=Trat, group = Trat), size=1.5, na.rm=T)+
    labs(x = ("Time (days)"), y = bquote("Clorophyll-a"~ (µg.L^-1))) +
  scale_x_discrete(breaks=c("T1", "T2","T3", "T4", "T5","T6","T7","T8","T9","T10"),
                   labels=c("1","2","3","4","5","6","7","8","9","10"))+
  theme_classic(base_size = 15)+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(legend.position="right")+
  theme(legend.title = element_blank())+
  guides(col = guide_legend(nrow = 2))+
  scale_color_grey()+
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd,
                    color=Trat,group = Trat), 
                width=1, position=position_dodge(.01))+
  guides(colour=guide_legend(nrow=3))+
  geom_ribbon(data = tidyr::pivot_wider(high,
                                        id_cols = c("Time", "Trat"), 
                                        names_from = "Trat", 
                                        values_from = c("mean", "sd")),
              aes(y = 1, ymax=mean_HCc, ymin=mean_HC, color = "HC", group = "HC"), 
              fill="gray", alpha=.5)

How to handle missing data in pandas dataframe?

copy iconCopydownload iconDownload
unique_id = df.timestamp.unique().tolist()
df_tmp = pd.DataFrame({'timestamp':unique_id,'position':range(4)})
df = pd.merge(df_tmp, df, on=["timestamp", "position"], how="left")
df.error.fillna(1)
-----------------------
>>> df['position'] = pd.Categorical(df['position'], categories=df['position'].unique())
>>> df_grouped = df.groupby(['timestamp', 't_idx', 'position'], as_index=False).first()
>>> df_grouped['error'] = df_grouped['error'].fillna(1)

>>> df_grouped.sort_values('type', inplace=True)
>>> df_grouped['type'] = df_grouped.groupby(['timestamp','t_idx'])['type'].ffill().bfill()

>>> df_grouped.sort_values('SNR', inplace=True)
>>> df_grouped['SNR'] = df_grouped.groupby(['timestamp','t_idx'])['SNR'].ffill().bfill()

>>> df_grouped = df_grouped.reset_index(drop=True)
    timestamp   t_idx   position    error   type    SNR
0   16229767    3       1           0.0     T9      38.0
1   16229767    3       3           1.0     T9      38.0
2   16229767    3       4           0.0     T9      38.0
3   16229767    5       2           1.0     T1      123.0
4   16229767    5       1           0.0     T1      123.0
5   16229767    5       3           0.0     T1      123.0
6   16229767    5       4           0.0     T1      123.0
7   29767162    7       1           0.0     T4      991.0
8   29767162    7       4           1.0     T4      991.0
9   16229767    3       2           1.0     T9      38.0
10  16229767    7       2           1.0     T4      991.0
11  16229767    7       1           1.0     T4      991.0
12  16229767    7       3           1.0     T4      991.0
13  16229767    7       4           1.0     T4      991.0
14  29767162    3       2           1.0     T4      991.0
15  29767162    3       1           1.0     T4      991.0
16  29767162    3       3           1.0     T4      991.0
17  29767162    3       4           1.0     T4      991.0
18  29767162    5       2           1.0     T4      991.0
19  29767162    5       1           1.0     T4      991.0
20  29767162    5       3           1.0     T4      991.0
21  29767162    5       4           1.0     T4      991.0
22  29767162    7       2           1.0     T4      991.0
23  29767162    7       3           1.0     T4      991.0
>>> df_grouped[
...     pd.Series(
...         list(zip(df_grouped['timestamp'].values, df_grouped['t_idx'].values))
...     ).isin(list(zip(df['timestamp'].values, df['t_idx'].values)))
... ].sort_values(by=['timestamp', 't_idx']).reset_index(drop=True)
    timestamp   t_idx   position    error   type    SNR
0   16229767    3       1           0.0     T9      38.0
1   16229767    3       3           1.0     T9      38.0
2   16229767    3       4           0.0     T9      38.0
3   16229767    3       2           1.0     T9      38.0
4   16229767    5       2           1.0     T1      123.0
5   16229767    5       1           0.0     T1      123.0
6   16229767    5       3           0.0     T1      123.0
7   16229767    5       4           0.0     T1      123.0
8   29767162    7       1           0.0     T4      991.0
9   29767162    7       4           1.0     T4      991.0
10  29767162    7       2           1.0     T4      991.0
11  29767162    7       3           1.0     T4      991.0
-----------------------
>>> df['position'] = pd.Categorical(df['position'], categories=df['position'].unique())
>>> df_grouped = df.groupby(['timestamp', 't_idx', 'position'], as_index=False).first()
>>> df_grouped['error'] = df_grouped['error'].fillna(1)

>>> df_grouped.sort_values('type', inplace=True)
>>> df_grouped['type'] = df_grouped.groupby(['timestamp','t_idx'])['type'].ffill().bfill()

>>> df_grouped.sort_values('SNR', inplace=True)
>>> df_grouped['SNR'] = df_grouped.groupby(['timestamp','t_idx'])['SNR'].ffill().bfill()

>>> df_grouped = df_grouped.reset_index(drop=True)
    timestamp   t_idx   position    error   type    SNR
0   16229767    3       1           0.0     T9      38.0
1   16229767    3       3           1.0     T9      38.0
2   16229767    3       4           0.0     T9      38.0
3   16229767    5       2           1.0     T1      123.0
4   16229767    5       1           0.0     T1      123.0
5   16229767    5       3           0.0     T1      123.0
6   16229767    5       4           0.0     T1      123.0
7   29767162    7       1           0.0     T4      991.0
8   29767162    7       4           1.0     T4      991.0
9   16229767    3       2           1.0     T9      38.0
10  16229767    7       2           1.0     T4      991.0
11  16229767    7       1           1.0     T4      991.0
12  16229767    7       3           1.0     T4      991.0
13  16229767    7       4           1.0     T4      991.0
14  29767162    3       2           1.0     T4      991.0
15  29767162    3       1           1.0     T4      991.0
16  29767162    3       3           1.0     T4      991.0
17  29767162    3       4           1.0     T4      991.0
18  29767162    5       2           1.0     T4      991.0
19  29767162    5       1           1.0     T4      991.0
20  29767162    5       3           1.0     T4      991.0
21  29767162    5       4           1.0     T4      991.0
22  29767162    7       2           1.0     T4      991.0
23  29767162    7       3           1.0     T4      991.0
>>> df_grouped[
...     pd.Series(
...         list(zip(df_grouped['timestamp'].values, df_grouped['t_idx'].values))
...     ).isin(list(zip(df['timestamp'].values, df['t_idx'].values)))
... ].sort_values(by=['timestamp', 't_idx']).reset_index(drop=True)
    timestamp   t_idx   position    error   type    SNR
0   16229767    3       1           0.0     T9      38.0
1   16229767    3       3           1.0     T9      38.0
2   16229767    3       4           0.0     T9      38.0
3   16229767    3       2           1.0     T9      38.0
4   16229767    5       2           1.0     T1      123.0
5   16229767    5       1           0.0     T1      123.0
6   16229767    5       3           0.0     T1      123.0
7   16229767    5       4           0.0     T1      123.0
8   29767162    7       1           0.0     T4      991.0
9   29767162    7       4           1.0     T4      991.0
10  29767162    7       2           1.0     T4      991.0
11  29767162    7       3           1.0     T4      991.0
-----------------------
def foo(df):
    set_ = set(range(1,5))
    if df.position.unique().size < 4:
        diff_ = set_.difference(df.position.unique())
        add_df = df.iloc[:len(diff_),:].copy()
        add_df.loc[:, 'position'] = list(diff_)
        # I did not understand by what rule the values in the error column are set. You can install it as you need
        result_df = pd.concat([df, add_df], ignore_index=True)
        return result_df
    else: 
        return df

group = df.groupby(['timestamp', 't_idx'])
group.apply(foo)

    timestamp   t_idx   position    error
0   16229767     3        3           1
1   16229767     3        1           0
2   16229767     3        4           0
3   16229767     3        2           1
4   16229767     5        2           1
5   16229767     5        1           0
6   16229767     5        3           0
7   16229767     5        4           0
8   29767162     7        1           0
9   29767162     7        4           1
10  29767162     7        2           0
11  29767162     7        3           1
-----------------------
def foo(df):
    set_ = set(range(1,5))
    if df.position.unique().size < 4:
        diff_ = set_.difference(df.position.unique())
        add_df = df.iloc[:len(diff_),:].copy()
        add_df.loc[:, 'position'] = list(diff_)
        # I did not understand by what rule the values in the error column are set. You can install it as you need
        result_df = pd.concat([df, add_df], ignore_index=True)
        return result_df
    else: 
        return df

group = df.groupby(['timestamp', 't_idx'])
group.apply(foo)

    timestamp   t_idx   position    error
0   16229767     3        3           1
1   16229767     3        1           0
2   16229767     3        4           0
3   16229767     3        2           1
4   16229767     5        2           1
5   16229767     5        1           0
6   16229767     5        3           0
7   16229767     5        4           0
8   29767162     7        1           0
9   29767162     7        4           1
10  29767162     7        2           0
11  29767162     7        3           1
-----------------------
# pip install pyjanitor
import pandas as pd
import janitor
(df.complete(['timestamp', 't_idx', 'type', 'SNR'], 'position')
   .fillna({"error":1}, downcast='infer')
   .filter(df.columns)
)
 
    timestamp  t_idx  position  error type  SNR
0    16229767      5         2      1   T1  123
1    16229767      5         1      0   T1  123
2    16229767      5         3      0   T1  123
3    16229767      5         4      0   T1  123
4    16229767      3         2      1   T9   38
5    16229767      3         1      0   T9   38
6    16229767      3         3      1   T9   38
7    16229767      3         4      0   T9   38
8    29767162      7         2      1   T4  991
9    29767162      7         1      0   T4  991
10   29767162      7         3      1   T4  991
11   29767162      7         4      1   T4  991

Measuring OpenMP Fork/Join latency

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#include <iostream>
#include <string>

#include <omp.h>

constexpr int n_warmup = 10'000;
constexpr int n_measurement = 100'000;
constexpr int n_spins = 1'000;

void spin() {
    volatile bool flag = false;
    for (int i = 0; i < n_spins; ++i) {
        if (flag) {
            break;
        }
    }
}

void bench_fork_join(int num_threads) {
    omp_set_num_threads(num_threads);

    // create threads, warmup
    for (int i = 0; i < n_warmup; ++i) {
        #pragma omp parallel
        spin();
    }

    double const start = omp_get_wtime();
    for (int i = 0; i < n_measurement; ++i) {
        #pragma omp parallel
        spin();
    }
    double const stop = omp_get_wtime();
    double const ptime = (stop - start) * 1e6 / n_measurement;

    // warmup
    for (int i = 0; i < n_warmup; ++i) {
        spin();
    }
    double const sstart = omp_get_wtime();
    for (int i = 0; i < n_measurement; ++i) {
        spin();
    }
    double const sstop = omp_get_wtime();
    double const stime = (sstop - sstart) * 1e6 / n_measurement;

    std::cout << ptime << " us\t- " << stime << " us\t= " << ptime - stime << " us\n";
}

int main(int argc, char **argv) {
    auto const params = argc - 1;
    std::cout << "parallel\t- sequential\t= overhead\n";

    for (int j = 0; j < params; ++j) {
        auto num_threads = std::stoi(argv[1 + j]);
        std::cout << "---------------- num_threads = " << num_threads << " ----------------\n";
        bench_fork_join(num_threads);
    }

    return 0;
}
$ g++ -fopenmp -O3 -DNDEBUG -o bench-omp-fork-join bench-omp-fork-join.cpp
$ ./bench-omp-fork-join 6 4 2 1
parallel        - sequential    = overhead
---------------- num_threads = 6 ----------------
1.51439 us      - 0.273195 us   = 1.24119 us
---------------- num_threads = 4 ----------------
1.24683 us      - 0.276122 us   = 0.970708 us
---------------- num_threads = 2 ----------------
1.10637 us      - 0.270865 us   = 0.835501 us
---------------- num_threads = 1 ----------------
0.708679 us     - 0.269508 us   = 0.439171 us
-----------------------
#include <iostream>
#include <string>

#include <omp.h>

constexpr int n_warmup = 10'000;
constexpr int n_measurement = 100'000;
constexpr int n_spins = 1'000;

void spin() {
    volatile bool flag = false;
    for (int i = 0; i < n_spins; ++i) {
        if (flag) {
            break;
        }
    }
}

void bench_fork_join(int num_threads) {
    omp_set_num_threads(num_threads);

    // create threads, warmup
    for (int i = 0; i < n_warmup; ++i) {
        #pragma omp parallel
        spin();
    }

    double const start = omp_get_wtime();
    for (int i = 0; i < n_measurement; ++i) {
        #pragma omp parallel
        spin();
    }
    double const stop = omp_get_wtime();
    double const ptime = (stop - start) * 1e6 / n_measurement;

    // warmup
    for (int i = 0; i < n_warmup; ++i) {
        spin();
    }
    double const sstart = omp_get_wtime();
    for (int i = 0; i < n_measurement; ++i) {
        spin();
    }
    double const sstop = omp_get_wtime();
    double const stime = (sstop - sstart) * 1e6 / n_measurement;

    std::cout << ptime << " us\t- " << stime << " us\t= " << ptime - stime << " us\n";
}

int main(int argc, char **argv) {
    auto const params = argc - 1;
    std::cout << "parallel\t- sequential\t= overhead\n";

    for (int j = 0; j < params; ++j) {
        auto num_threads = std::stoi(argv[1 + j]);
        std::cout << "---------------- num_threads = " << num_threads << " ----------------\n";
        bench_fork_join(num_threads);
    }

    return 0;
}
$ g++ -fopenmp -O3 -DNDEBUG -o bench-omp-fork-join bench-omp-fork-join.cpp
$ ./bench-omp-fork-join 6 4 2 1
parallel        - sequential    = overhead
---------------- num_threads = 6 ----------------
1.51439 us      - 0.273195 us   = 1.24119 us
---------------- num_threads = 4 ----------------
1.24683 us      - 0.276122 us   = 0.970708 us
---------------- num_threads = 2 ----------------
1.10637 us      - 0.270865 us   = 0.835501 us
---------------- num_threads = 1 ----------------
0.708679 us     - 0.269508 us   = 0.439171 us
-----------------------
n=   80'000    fork_join_latency<0.000001
n=  800'000    fork_join_latency=0.000036
n= 8'000'000   fork_join_latency=0.000288
n=80'000'000   fork_join_latency=0.003236
double totalSyncTime = 0.0;

void action1(std::vector<double>& t1)
{
    constexpr int threadCount = 6;
    double timePerThread[threadCount] = {0};

    #pragma omp parallel
    {
        const double start = omp_get_wtime();
        #pragma omp for nowait schedule(static) //num_threads(std::thread::hardware_concurrency())
        #pragma nounroll
        for (auto index = std::size_t{}; index < t1.size(); ++index)
        {
            t1.data()[index] = std::sin(t1.data()[index]);
        }
        const double stop = omp_get_wtime();
        const double threadLoopTime = (stop - start);
        timePerThread[omp_get_thread_num()] = threadLoopTime;
    }

    const double mini = *std::min_element(timePerThread, timePerThread+threadCount);
    const double maxi = *std::max_element(timePerThread, timePerThread+threadCount);
    const double syncTime = maxi - mini;
    totalSyncTime += syncTime;
}
-----------------------
n=   80'000    fork_join_latency<0.000001
n=  800'000    fork_join_latency=0.000036
n= 8'000'000   fork_join_latency=0.000288
n=80'000'000   fork_join_latency=0.003236
double totalSyncTime = 0.0;

void action1(std::vector<double>& t1)
{
    constexpr int threadCount = 6;
    double timePerThread[threadCount] = {0};

    #pragma omp parallel
    {
        const double start = omp_get_wtime();
        #pragma omp for nowait schedule(static) //num_threads(std::thread::hardware_concurrency())
        #pragma nounroll
        for (auto index = std::size_t{}; index < t1.size(); ++index)
        {
            t1.data()[index] = std::sin(t1.data()[index]);
        }
        const double stop = omp_get_wtime();
        const double threadLoopTime = (stop - start);
        timePerThread[omp_get_thread_num()] = threadLoopTime;
    }

    const double mini = *std::min_element(timePerThread, timePerThread+threadCount);
    const double maxi = *std::max_element(timePerThread, timePerThread+threadCount);
    const double syncTime = maxi - mini;
    totalSyncTime += syncTime;
}

Surf dependency causes &quot;cannot be shared between threads safely&quot; error in previously compiling program with matrix_sdk and async-trait

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info!("Registering to {}", self.homeserver().await);
let homeserver = self.homeserver().await;
info!("Registering to {}", homeserver);
-----------------------
info!("Registering to {}", self.homeserver().await);
let homeserver = self.homeserver().await;
info!("Registering to {}", homeserver);

Passenger keeps looking for wrong ruby version

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passenger-config about ruby-command
passenger-config was invoked through the following Ruby interpreter:
  Command: /home/deploy/.rvm/gems/ruby-2.6.5/wrappers/ruby
server {
 ....
 passenger_ruby /home/deploy/.rvm/gems/ruby-2.6.5/wrappers/ruby
-----------------------
passenger-config about ruby-command
passenger-config was invoked through the following Ruby interpreter:
  Command: /home/deploy/.rvm/gems/ruby-2.6.5/wrappers/ruby
server {
 ....
 passenger_ruby /home/deploy/.rvm/gems/ruby-2.6.5/wrappers/ruby

What is the name of the default code model used by gcc for MIPS 64?

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1:   jalr   $25

replaceAll function in javscript doesn't replace all occurrences

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const paragraph = '1\t1\t150\t18\t\"Pack of 12 action figures (variety)\"\t18\t9\t5.50\t2013-01-02 00:00:00.0000000\tTrue\t6\t2013-01-02 07:00:00.0000000\r\n2\t1\t151\t21\t\"Pack of 12 action figures (male)\"\t21\t9\t5.50\t2013-01-02 00:00:00.0000000\tTrue\t6\t2013-01-02 07:00:00.0000000\r\n3\t1\t152\t18\t\"Pack of 12 action figures (female)\"\t18\t9\t5.50\t2013-01-02 00:00:00.0000000\tTrue\t6\t2013-01-02 07:00:00.0000000\r\n4\t2\t76\t8\t\"\\\"The ';
const regex = /("[^"]*")|\t/g;
const found = paragraph.replace(regex, (whole_match,group_one) => group_one || ',' );

console.log(found);

Best option to search for characters in a column using R

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codes <- c("s001", "s1234", "s4g6", "T002", "T191","t985","s761","t17.5")

ifelse(grepl("^s[01]|^T[019]", codes), 1, 0)
[1] 1 1 0 1 1 0 0 0
as.numeric(grepl("^s[01]|^T[019]", codes))
-----------------------
codes <- c("s001", "s1234", "s4g6", "T002", "T191","t985","s761","t17.5")

ifelse(grepl("^s[01]|^T[019]", codes), 1, 0)
[1] 1 1 0 1 1 0 0 0
as.numeric(grepl("^s[01]|^T[019]", codes))
-----------------------
codes <- c("s001", "s1234", "s4g6", "T002", "T191","t985","s761","t17.5")

ifelse(grepl("^s[01]|^T[019]", codes), 1, 0)
[1] 1 1 0 1 1 0 0 0
as.numeric(grepl("^s[01]|^T[019]", codes))
-----------------------
+(grepl("^s[01]|^T[019]", codes))
[1] 1 1 0 1 1 0 0 0
-----------------------
library(dplyr)
library(stringr)

# your dataframe with codes column
df <- data.frame(codes = c("s001", "s1234", "s4g6", 
                              "T002", "T191","t985",
                              "s761","t17.5"))

# define what you want to search for
search_pattern <- "S0|S1|T0|T9|T1"

# check with `str_detect`
df %>% 
    mutate(check = ifelse(str_detect(df$codes, search_pattern)==TRUE, 1, 0)) 

  codes check
1  s001     0
2 s1234     0
3  s4g6     0
4  T002     1
5  T191     1
6  t985     0
7  s761     0
8 t17.5     0
-----------------------
library(dplyr)
library(stringr)

# your dataframe with codes column
df <- data.frame(codes = c("s001", "s1234", "s4g6", 
                              "T002", "T191","t985",
                              "s761","t17.5"))

# define what you want to search for
search_pattern <- "S0|S1|T0|T9|T1"

# check with `str_detect`
df %>% 
    mutate(check = ifelse(str_detect(df$codes, search_pattern)==TRUE, 1, 0)) 

  codes check
1  s001     0
2 s1234     0
3  s4g6     0
4  T002     1
5  T191     1
6  t985     0
7  s761     0
8 t17.5     0
-----------------------
> +grepl("^([sT][01]|T9)", codes)
[1] 1 1 0 1 1 0 0 0
-----------------------
codes = c("s001", "s1234", "s4g6", "T002", "T191","t985","s761","t17.5")

correct_values <- c("s0", "s1", "T0", "T9", "T1")

as.integer(substr(codes, 1, 2) %in% correct_values)
#[1] 1 1 0 1 1 0 0 0

TraMineR graphics not reactive to layout of R

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myheatmap <- heatmap
edit(myheatmap)

Community Discussions

Trending Discussions on t9
  • Print line that meets conditions in different lines with awk
  • Create subset of data frame to indicate limits in geom_ribbon
  • How to handle missing data in pandas dataframe?
  • Measuring OpenMP Fork/Join latency
  • Surf dependency causes &quot;cannot be shared between threads safely&quot; error in previously compiling program with matrix_sdk and async-trait
  • Passenger keeps looking for wrong ruby version
  • What is the name of the default code model used by gcc for MIPS 64?
  • Alloy assertion on implies command
  • replaceAll function in javscript doesn't replace all occurrences
  • MIPS 32 showing wrong values and repeat print statements
Trending Discussions on t9

QUESTION

Print line that meets conditions in different lines with awk

Asked 2022-Apr-10 at 10:17

total newbie. It is possible with awk to print the following:

w171930 t1 Y z2545377 <--- print this line
w171930 t2 X z4495648
w171931 t1 Y z2555698 <--- print this line
w171931 t2 X z5505690
w171932 t1 Y z2554345 <--- print this line
w171932 t2 X z5507345
w171933 t1 Y z2214694
w171933 t2 Y z8022710
w171933 t3 Y z2143462
w171933 t4 Y z6217556
w171933 t5 Y z9608343
w171933 t6 Y z9984446
w171933 t7 Y z2985572
w171933 t8 Y z6334512 <--- print this line
w171933 t9 X z6503375
w171943 t1 Y z2441603 <--- NO print this line
w171943 t2 X z4644534
w171943 t3 Y z2164440
w171944 t1 Y z2165532

Search on $3 == "X" and print the previous line if the next line has a $2 == "t1"

Goal

w171930 t1 Y z2545377
w171931 t1 Y z2555698
w171932 t1 Y z2554345
w171933 t8 Y z6334512

I have only managed to print the previous line but I don't know how to perform the complete condition

awk '$3 == "Y" { Y=$0; next; } { if ($3 =="X") print Y;}'

I'm sorry if I don't express myself correctly

enter image description here

ANSWER

Answered 2022-Apr-09 at 09:55
awk '$2=="t1"{ if(prev2!="" && prev!="") print prev2 }
     { prev=($3=="X"?prev2:""); prev2=$0 }' input
  • $2=="t1" only for lines where the second element = "t1". When prev and prev2 have a value, then we should print the value.
  • prev will contain the value of prev2 when the third element is equal to "X"
  • prev2 will have the value of the line which needs to be returned.

Source https://stackoverflow.com/questions/71806898

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install t9

You can download it from GitHub.
You can use t9 like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the t9 component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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