I'm using orientdb 2.2.14 with scala. The connections aren't closed and application crashes. The following test displays the problem
val poolFactory = new OPartitionedDatabasePoolFactory()
poolFactory.setMaxPoolSize(40)
val pool = poolFactory.get("remote:db.test.com.br/mydb", "admin", "password")
val conn = pool.acquire()
try {
val query = StringBuilder.newBuilder
query.append("SELECT #this.tojson(\"fetchPlan:*:-1\") AS itens FROM Parceiro limit 1000;")
val res = conn.query[java.util.List[ODocument]](new OSQLSynchQuery[ODocument](query.toString)).asScala.toList
val result = {
if (res.isEmpty) {
None
} else {
Some(res(0).field("itens"))
}
}
result
} finally {
conn.close()
pool.close()
poolFactory.close()
}
After execute, connection stays alive. The pool close dosen't work.
netstat -ant | fgrep 2424
tcp6 0 0 :::2424 :::* LISTEN
tcp6 0 0 172.17.0.1:51928 172.17.0.1:2424 ESTABLISHED
When I do same test with postgreSQL DB the result is:
netstat -ant | fgrep 5432
tcp 0 0 0.0.0.0:5432 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:5432 127.0.0.1:36988 TIME_WAIT
tcp6 0 0 :::5432 :::* LISTEN
Related
I am trying to echo some string to /dev/ttyS0 and the rx and tx stats in /proc/tty/driver/serial remain 0.
If I change the irq of ttyS0 to 0 and then echo some string to ttyS0, the stats are changing according to the length of the string.
Would anyone be able to help me with this?
output of setserial -g /dev/ttyS[0123]
/dev/ttyS0, UART: 16550A, Port: 0x03f8, IRQ: 11
/dev/ttyS1, UART: 16550A, Port: 0x02f8, IRQ: 10
/dev/ttyS2, UART: unknown, Port: 0x03e8, IRQ: 4
/dev/ttyS3, UART: unknown, Port: 0x02e8, IRQ: 3
output of stty -F /dev/ttyS0 -a
speed 115200 baud; rows 24; columns 80; line = 0;
intr = ^C; quit = ^\; erase = ^?; kill = ^U; eof = ^D; eol = <undef>; eol2 = <undef>; swtch = <undef>; start = ^Q; stop = ^S;
susp = ^Z; rprnt = ^R; werase = ^W; lnext = ^V; discard = ^O; min = 1; time = 0;
-parenb -parodd -cmspar cs8 hupcl -cstopb cread clocal -crtscts
-ignbrk -brkint -ignpar -parmrk -inpck -istrip -inlcr -igncr -icrnl -ixon -ixoff -iuclc -ixany -imaxbel iutf8
opost -olcuc -ocrnl onlcr -onocr -onlret -ofill -ofdel nl0 cr0 tab0 bs0 vt0 ff0
-isig -icanon -iexten -echo -echoe -echok -echonl -noflsh -xcase -tostop -echoprt -echoctl -echoke -flusho -extproc
out put of cat /proc/interrupts for irq 11 with 16 cpu.
11: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 IO-APIC 11-edge ttyS0
output of cat /proc/tty/driver/serial:
0: uart:16550A port:000003F8 irq:11 tx:228 rx:0 RTS|DTR|DSR|CD|RI
1: uart:16550A port:000002F8 irq:10 tx:0 rx:0 CTS|DSR|CD|RI
2: uart:unknown port:000003E8 irq:4
3: uart:unknown port:000002E8 irq:3
As i said I have tried to change thee irq to 0 and it worked. But what is wrong with irq 11. There is no deviced registered for irq 11 in /proc/interrupts except ttyS0.
I am trying to iterate through a Dask dataframe and compare the values in one of its columns to a column in another Dask dataframe with the same name. If the columns match I would like to update the value is the target Dask dataframe. The code below runs, but the values are not updated to '1' where I expected, or anywhere. I am new to Dask and suspect I am missing some crucial step or am not understanding the framework.
def populateSymptomsDDF(row):
for vac in row['vac_codes']:
if vac in symptoms_ddf.columns:
symptoms_ddf[vac] = symptoms_ddf[vac].where(symptoms_ddf['dog'] == row['dog'], 1)
with ProgressBar():
x = vac_ddf.apply(lambda x: populateSymptomsDDF(x), meta=('int64'), axis=1)
x.compute(scheduler='processes')
symptoms_ddf.compute()
Head of icd_ddf:
dog vac_codes
0 1 [G35, E11.40, R53.1, Z79.899, I87.2]
1 2 [G35, R53.83, G47.00]
2 3 [G35, G95.9, R53.83, F41.9]
3 4 [G35, N53.9, E55.9, Z74.09]
4 5 [G35, M51.26, R53.1, M47.816, R25.2, G82.50, R...
Head of symptoms_ddf (before running code):
dog W19 W10 W05.0 V00.811 R53.83 R53.8 R53.1 R47.9 R47.89 ... G81.12 G81.11 G81.10 G50.0 G31.84 F52.8 F52.31 F52.22 F52.0 F03
0 1 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
1 2 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
2 3 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
3 4 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
4 5 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Thank you for any insights you can provide!
Dask dataframes don't have the same in-place behavior as pandas. Generally every operation should be a bulk parallel operation. Otherwise there isn't much reason to use Dask.
Also, iterating through dataframes will generally be quite slow. This is also true with Pandas.
Fortunately, I think that you're maybe just looking for a join or merge operation. I would encourage you to look up the documentation for Pandas merge
https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
I have been trying to solve this problem of titanic survived problem. Where i splitted x to be the passengers and y to be the survived. But the problem is i couldn't able to get the y_pred (ie) prediction results. As it is 0 for all the values. I get 0 value as prediction. It would be helpful for me if anyone can solve it. As it is my first classifier problem as a beginner
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('C:/Users/Umer/train.csv')
x = df['PassengerId'].values.reshape(-1,1)
y = df['Survived']
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25,
random_state = 0)
from sklearn.preprocessing import StandardScaler
sc_x = StandardScaler()
x_train = sc_x.fit_transform(x_train)
x_test = sc_x.transform(x_test)
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(x_train,y_train)
#predicting the test set results
y_pred = classifier.predict(x_test)
I couldn't reproduce the same result, in fact, I copied-pasted your code and did not get them all zeros as you described the issue as, instead I got:
[0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0]
Nevertheless, there are a few things I noticed in your approach that you may want to know about:
The default separator in Pandas read_csv is , , so if your dataset variables separated by a tab (same like the one I have) , you then should specify the separator like this:
df = pd.read_csv('titanic.csv', sep='\t')
PassengerId has no useful information that your model may learn from in order to predict the Survived people, it's just a continuous number that increments with each new passenger. Generally speaking, in classification, you need to avail of all features that make your model learns from (unless of course there are redundant features that add no information to the model) especially in your dataset, it's a multivariate dataset.
There is no point of scaling the PassengerId, because features scaling is usually used when features highly vary in magnitudes, units and range (e.g. 5kg and 5000gms) and in your case, as I mentioned, it's just an incremental integer which has no real information to the model.
One last thing, you should get your data as type float for StandardScaler to avoid warnings like the follow:
DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.
So you do convert like this from the beginning:
x = df['PassengerId'].values.astype(float).reshape(-1,1)
Finally if you're still getting the same result, then please add a link to your dataset.
Update
After providing the dataset, it turns out that the result you're getting is correct, that's again because of reason number 2 I mentioned above (that is PassengerId provides no useful information to the model so it cannot predict correctly!)
You can test it yourself via comparing the log loss before and after adding more features from the dataset:
from sklearn.metrics import log_loss
df = pd.read_csv('train.csv', sep=',')
x = df['PassengerId'].values.reshape(-1,1)
y = df['Survived']
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25,
random_state = 0)
classifier = LogisticRegression()
classifier.fit(x_train,y_train)
y_pred_train = classifier.predict(x_train)
# calculate and print the loss function using only the PassengerId
print(log_loss(y_train, y_pred_train))
#predicting the test set results
y_pred = classifier.predict(x_test)
print(y_pred)
Output
13.33982681120802
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0]
Now by using many "supposedly-useful" information:
from sklearn.metrics import log_loss
df = pd.read_csv('train.csv', sep=',')
# denote the words female and male as 0 and 1
df['Sex'].replace(['female','male'], [0,1], inplace=True)
# try three features that you think they are informative to the model
# so it can learn from them
x = df[['Fare', 'Pclass', 'Sex']].values.reshape(-1,3)
y = df['Survived']
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25,
random_state = 0)
classifier = LogisticRegression()
classifier.fit(x_train,y_train)
y_pred_train = classifier.predict(x_train)
# calculate and print the loss function with the above 3 features
print(log_loss(y_train, y_pred_train))
#predicting the test set results
y_pred = classifier.predict(x_test)
print(y_pred)
Output
7.238735137632405
[0 0 0 1 1 0 1 1 0 1 0 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 1 0 0 0
0 1 1 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 0
1 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1
1 0 0 1 1 0 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0
0 1 0 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 1
1]
In Conclusion:
As you can see, the loss gave better value (lesser than before) and the prediction is now more reasonable!
Please find my code below.
str = "1791 (AR6K Async) S 2 0 0 0 -1 2129984 0 0 0 0 0 113 0 0 20 0 1 0 2370 0 0 4294967295 0 0 0 0 0 0 0 2147483647 0 3221520956 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0"
for val in str:gmatch("%S+") do
print(val)
end
Output:
1791
(AR6K
Async)
S
2
0
0
0
-1
....
But I am expecting the output like,
1791
(AR6K Async)
S
2
0
0
0
-1
...
Can anyone please help me how to get the values in bracket as a single value instead getting separate values.
str = "1791 (AR6K Async) S 2 0 0 0 -1 2129984 0 0 0 0 0 113 0 0 20 0 1 0 2370 0 0 4294967295 0 0 0 0 0 0 0 2147483647 0 3221520956 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0"
for val in str:gsub("%S+","\0%0\0")
:gsub("%b()", function(s) return s:gsub("%z","") end)
:gmatch("%z(.-)%z") do
print(val)
end
Explanation:
1. Surround all spaceless substrings with "zero marks"
(add one binary-zero-character at the beginning and one at the end)
2. Remove "zero marks" from inside parentheses
3. Display all surrounded parts
It may not be possible to use a single lua pattern alone to do this.
However it can be easy to roll your own parsing / splitting of the string or just extend your code a bit to concatenate the parts from a part that starts with ( to the part that ends with )
Here is a small example
str = "1791 (AR6K Async) S 2 0 0 0 -1 2129984 0 0 0 0 0 113 0 0 20 0 1 0 2370 0 0 4294967295 0 0 0 0 0 0 0 2147483647 0 3221520956 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0"
local temp
for val in str:gmatch("%S+") do
if temp then
if val:sub(#val, #val) == ")" then
print(temp.." "..val)
temp = nil
else
temp = temp.." "..val
end
elseif val:sub(1,1) == "(" then
temp = val
else
print(val)
end
end
This code behaves exactly like your own, except that when it encounters a substring that starts with an opening bracket, it will save it to temp variable. Then it will concatenate new values to temp until a substring with the closing bracket at the end of it is encountered. At that point the whole string saved to temp will be printed and temp is set to nil and the loop is continued normally.
So there is just a special case coded for when a string with brackets comes by.
This may not be the most efficient implementation, but it works. Also this assumes that the separators are spaces, since when the strings are concatenated to temp variable, they will be concatenated with an ordinary space. This does not handle nested brackets.
This was just a quick demonstration of the idea however so I believe you can fix these shortcomings on your own as you need to if you use it.
What is the best way to extract the two fields (I have marked them in parenthesis) in the following line on a Linux system.
<measResults>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 311133336 325126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10427349176 3527288 284344 439048 3582 3583 0 0 0 0 0 0 0 0 14422 14422 (311133336) 325126 (10427349176) 3527288 </measResults>
This is a single line in a *.xml file.
I am hoping some variant of grep would work but I am open to suggestions.
Are they always at the same position (i.e. after the same amount of spaces)? If so, I would use awk:
# echo "$string" | awk '{ print $51 }'
(311133336)
This should do:
awk '{print $51,$53}' <<< "$string"
(311133336) (10427349176)