So I have like kind of a homemade economy thing and when I added a working command and when it works it picks a random number and stuff but when it adds the number to the old currency it's adding like for ex 123+123 = 123123 when it supposed to be 246 I don't know how to fix this I tried everything and nothing has worked for me
if (message.content.toLowerCase().startsWith(prefixS.prefix + 'work')) {
const inventoryS = await inventory.findOne({ userID: message.author.id, guildID: message.guild.id });
if (inventoryS.item1 === 'true') {
const payment = Math.floor(Math.random() * 125) + 25;
inventoryS.currency += payment
inventoryS.save()
message.channel.send({ embeds: [new Discord.MessageEmbed().setTitle('After a long day of work your balance is').setDescription(`__Your balance__\n > Money: ${inventoryS.currency}`).setColor('BROWN')] })
}
}
Both of them must be Numbers to do addition, otherwise it will add as strings
inventoryS.currency = parseInt(inventoryS.currency) + parseInt(payment)
it is possible that your inventoryS.currency is a string value
let a = '123' //a string
let b = 123 //an integer
a += b
console.log(a) //logs 123123
you will need to parse it to an integer before adding
let a = '123'
let b = 123
a = parseInt(a) + b
console.log(a)
more references here
I can not find a library to use PAM (K-medoids) in Pyspark.
I have found this in Scala :
https://gist.github.com/erikerlandson/c3c35f0b1aae737fc884
And this issue in Spark which was resolved in 2016 :
https://issues.apache.org/jira/browse/SPARK-4510
https://github.com/apache/spark/pull/3382
But it seems not to be working and this is not included in the mllib documentation :
http://spark.apache.org/docs/2.0.0/api/python/pyspark.mllib.html#module-pyspark.mllib.clustering
Does anyone knows any library for PAM in Pyspark ?
Thank you
I actually had a go at this the other day for fun. Can't say much about performance as I'm quite new to spark. But here is KMedoids with K++ seeding:
# (c) 2020 Jonathan Kelsey
# This code is licensed under MIT license
from pyspark.sql import functions as F
import pyspark
import numpy as np
import sys
def seed_kernel(data_broadcast, data_id_value, centeroids, k, metric):
data = data_broadcast.value
point = data_id_value[1]
min_distance = sys.maxsize
for j in range(len(centeroids)):
distance = metric(point, data[centeroids[j]])
min_distance = min(min_distance, distance)
return min_distance
def seed_clusters(data_broadcast, data_frame, k, metric):
data = data_broadcast.value
centeroids = list(np.random.choice(data.shape[0], 1, replace=False))
for i in range(k - 1):
print("clusterSeed", i)
distances = []
mK = data_frame.rdd.map(lambda data_id_value: seed_kernel(data_broadcast, data_id_value, centeroids, k, metric))
mK_collect = mK.collect()
distances = np.array(mK_collect)
next_centeroid = np.argmax(distances)
centeroids.append(next_centeroid)
print("centeroids", centeroids)
return centeroids
def nearest_centeroid_kernel(data_id_value, centeroid_id_values, metric):
_, data_value = data_id_value
data_np = np.asarray(data_value)
distances = []
for _, centeroid_value in centeroid_id_values:
centeroid_np = np.asarray(centeroid_value)
distance = metric(data_np, centeroid_np)
distances.append(distance)
distances = np.asarray(distances)
closest_centeroid = np.argmin(distances)
return int(closest_centeroid)
def optimise_cluster_membership_spark(data, data_frame, n, metric, intital_cluster_indices=None):
data_shape = data.shape
data_rdd = data_frame.rdd
data_length = data_shape[0]
if intital_cluster_indices is None:
index = np.random.choice(data_length, n, replace=False)
else:
index = intital_cluster_indices
list_index = [int(i) for i in list(index)]
centeroid_id_values = [(i,data[index[i]]) for i in range(len(index))]
data_rdd = data_rdd.filter(lambda data_id_value: int(data_id_value["id"]) not in list_index)
associated_cluster_points = data_rdd.map(lambda data_id_value: (data_id_value[0],nearest_centeroid_kernel(data_id_value, centeroid_id_values, metric)))
clusters = associated_cluster_points.toDF(["id", "bestC"]).groupBy("bestC").agg(F.collect_list("id").alias("cluster"))
return index, clusters
def cost_kernel(data_broadcast, test_centeroid, cluster_data, metric):
data = data_broadcast.value
cluster = np.asarray(cluster_data)
cluster_length = cluster.shape[0]
feature_length = data.shape[1]
test_centeroid_column = np.zeros(shape=(cluster_length, feature_length), dtype=data.dtype)
new_cluster_column = np.zeros(shape=(cluster_length, feature_length), dtype=data.dtype)
for i in range(0, cluster_length):
new_cluster_column[i] = data[cluster[i]]
test_centeroid_column[i] = data[int(test_centeroid)]
pairwise_distance = metric(new_cluster_column, test_centeroid_column)# (np.absolute(new_cluster_column-test_centeroid_column).sum(axis=1))# metric(new_cluster_column, test_centeroid_column)
cost = np.sum(pairwise_distance)
return float(cost) #new_cluster_column.shape[1]
def optimise_centroid_selection_spark(data_broadcast, data_frame, centeroids, clusters_frames, metric):
data = data_broadcast.value
new_centeroid_ids = []
total_cost = 0
for cluster_idx in range(len(centeroids)):
old_centeroid = centeroids[cluster_idx]
cluster_frame = clusters_frames.filter(clusters_frames.bestC == cluster_idx).select(F.explode(clusters_frames.cluster))
cluster_data = cluster_frame.collect()
if cluster_data:
cluster_data = [cluster_data[i].col for i in range(len(cluster_data))]
else:
cluster_data = []
cost_data = cluster_frame.rdd.map(lambda point_id: (point_id[0], cost_kernel(data_broadcast, point_id[0], cluster_data, metric)))
cost = cost_data.map(lambda point_id_cost: point_id_cost[1]).sum()
total_cost = total_cost + cost
point_result = cost_data.sortBy(lambda point_id_cost: point_id_cost[1]).take(1)
if (point_result):
best_point = point_result[0][0]
else:
best_point = old_centeroid
new_centeroid_ids.append(best_point)
return (new_centeroid_ids, total_cost)
def validate_metric(metric):
if (metric == "euclidean" or metric == "hamming"):
return True
if isinstance(metric, dict) == False:
return "Metric is not a dictionary. And not a known string 'euclidean' or 'hamming'"
metric_keys = metric.keys()
if "point" not in metric_keys or "vector" not in metric_keys:
return "Metric does not contain a member function for 'point' and/or 'point'."
if callable(metric["point"]) == False or callable(metric["vector"]) == False:
return "Metric.point and/or Metric.vector are not callable functions."
if (metric["point"].__code__.co_argcount != 2 and metric["vector"].__code__.co_argcount != 2):
return "Metric.point and/or Metric.vector do not both have 2 arguments."
return True
# pre-defined metrics
#vector metrics
def hamming_vector(stack1, stack2):
return (stack1 != stack2).sum(axis=1)
def euclidean_vector(stack1, stack2):
#return (np.absolute(stack2-stack1)).sum(axis=1)
return np.sqrt(((stack2-stack1)**2).sum(axis=1))
# point metrics
def hamming_point(p1, p2):
return np.sum((p1 != p2))
def euclidean_point(p1, p2):
return np.sqrt(np.sum((p1 - p2)**2))
def fit(sc, data, n_clusters = 2, metric = "euclidean", seeding = "heuristic"):
metric_valid = validate_metric(metric)
if metric_valid == True:
if metric == "euclidean":
point_metric = euclidean_point
vector_metric = euclidean_vector
elif metric == "hamming":
point_metric = hamming_point
vector_metric = hamming_vector
else:
point_metric = metric["point"]
vector_metric = metric["vector"]
else:
print(metric_valid)
return
data_np = np.asarray(data)
data_broadcast = sc.broadcast(data_np)
seeds = None
data_frame = sc.parallelize(data).zipWithIndex().map(lambda xy: (xy[1],xy[0])).toDF(["id", "vector"]).cache()
if (seeding == "heuristic"):
seeds = list(seed_clusters(data_broadcast, data_frame, n_clusters, point_metric))
last_centeroids, last_clusters = optimise_cluster_membership_spark(data_np, data_frame, n_clusters, point_metric, seeds)
last_cost = float('inf')
iteration = 0
escape = False
while not escape:
iteration = iteration + 1
current_centeroids, current_cost = optimise_centroid_selection_spark(data_broadcast, data_frame, last_centeroids, last_clusters, vector_metric)
current_centeroids, current_clusters = optimise_cluster_membership_spark(data_np, data_frame, n_clusters, point_metric, current_centeroids)
print((current_cost<last_cost, current_cost, last_cost, current_cost - last_cost))
if (current_cost<last_cost):
print(("iteration",iteration,"cost improving...", current_cost, last_cost, current_centeroids))
last_cost = current_cost
last_centeroids = current_centeroids
last_clusters = current_clusters
else:
print(("iteration",iteration,"cost got worse or did not improve", current_cost, last_cost))
escape = True
bc = last_clusters.sort("bestC", ascending=True).collect()
unpacked_clusters = [bc[i].cluster for i in range(len(bc))]
return (last_centeroids, unpacked_clusters)
I used some sample data from pyclustering as a sanity check:
from pyclustering.cluster import cluster_visualizer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import FCPS_SAMPLES
from pyclustering.samples.definitions import SIMPLE_SAMPLES
sample = read_sample(FCPS_SAMPLES.SAMPLE_GOLF_BALL)
bestCentroids, bestClusters = fit(sc, sample, 9)
visualizer = cluster_visualizer()
visualizer.append_clusters(bestClusters, sample)
visualizer.show()
Your best choice is to adapt this Python implementation into Scala so you take advance of RDD partitions and distributed computation.
https://github.com/letiantian/kmedoids/blob/master/kmedoids.py
I have created a google app engine application called Ascii Art at this link http://ascii-chan-1018.appspot.com/ but for some reason sometimes when you post something it gives you this error code on the screen
"500 Internal Server Error The server has either erred or is incapable of performing the requested operation." Sometimes it works and sometimes it gives you this error code. I'm not sure if its my source code or if its an error on google's servers.
import os
import re
import sys
import urllib2
import random
import logging
from xml.dom import minidom
from string import letters
import webapp2
import jinja2
from google.appengine.api import memcache
from google.appengine.ext import db
template_dir = os.path.join(os.path.dirname(__file__), 'templates')
jinja_env = jinja2.Environment(loader = jinja2.FileSystemLoader(template_dir), autoescape=True)
art_key = db.Key.from_path('ASCIIChan', 'arts')
def console(s):
sys.stderr.write('%s\n' % s)
IP_URL = "http://api.hostip.info/?ip="
def get_coords(ip):
ip = "17.173.254.223"
url = IP_URL + ip
content = None
try:
content = urllib2.urlopen(url).read()
except URLError:
return
if content:
d = minidom.parseString(content)
coords = d.getElementsByTagName("gml:coordinates")
if coords and coords[0].childNodes[0].nodeValue:
lon, lat = coords[0].childNodes[0].nodeValue.split(',')
return db.GeoPt(lat, lon)
class Handler(webapp2.RequestHandler):
def write(self, *a, **kw):
self.response.out.write(*a, **kw)
def render_str(self, template, **params):
t = jinja_env.get_template(template)
return t.render(params)
def render(self, template, **kw):
self.write(self.render_str(template, **kw))
GMAPS_URL = "http://maps.googleapis.com/maps/api/staticmap?size=380x263&sensor=false&"
def gmap_img(points):
markers = '&'.join('markers=%s,%s' % (p.lat, p.lon) for p in points)
return GMAPS_URL + markers
class Art(db.Model):
title = db.StringProperty(required = True)
art = db.TextProperty(required = True)
created = db.DateTimeProperty(auto_now_add = True)
coords = db.GeoPtProperty( )
def top_arts(update = False):
key = 'top'
arts = memcache.get(key)
if arts is None or update:
logging.error("DB QUERY")
arts = db.GqlQuery("SELECT * "
"FROM Art "
"WHERE ANCESTOR IS :1 "
"ORDER BY created DESC "
"LIMIT 10",
art_key)
arts = list(arts)
memcache.set(key, arts)
return arts
class MainPage(Handler):
def render_front(self, title="", art="", error=""):
arts = top_arts()
img_url = None
points = filter(None, (a.coords for a in arts))
if points:
img_url = gmap_img(points)
#display the image URL
self.render("Ascii.html", title = title, art = art, error = error, arts = arts, img_url = img_url)
def get(self):
self.render_front()
def post(self):
title = self.request.get("title")
art = self.request.get("art")
if title and art:
p = Art(parent=art_key, title = title, art = art)
#lookup the user's coordinates from their IP
coords = get_coords(self.request.remote_addr)
#if we have coordinates, add them to the art
if coords:
p.coords = coords
p.put()
#rerun the query and update the cache
top_arts(True)
self.redirect("/")
else:
error = "Invalid, are you sure you entered a title and art work?"
self.render_front(error = error, title = title, art =art)
app = webapp2.WSGIApplication([('/', MainPage)])
Well I'm not sure if you can consider it solving the problem but I just removed the google maps code that was left over. It kept complaining about the line except URLError, so after I removed the maps code it worked fine, and my initial plan was to remove it anyways.
i have this code in homepage
CheckBox[] ch= new CheckBox[12];
ch[0] = ChkContextA;
ch[1]= ChkContextB;
ch[2]= ChkContextC;
ch[3]= ChkContextD;
ch[4]= ChkContextE;
ch[5]= ChkContextF;
ch[6]= ChkContextG;
ch[7]= ChkContextH;
ch[8]= ChkContextI;
ch[9]= ChkContextJ;
ch[10]= ChkContextK;
ch[11]= ChiContextL;
for (int i = 0; i < 11; i++)
if (ch[i].Checked) search += ch[i].Text + " ";
Response.Redirect("SearchEstate.aspx?content="+search);
and this code in SearchEstate
var content = Request.QueryString["content"];
RealEstateEntities db = new RealEstateEntities();
var query = from O in db.Owners
join E in db.Estates on O.OwnerID equals E.OwnerID
join P in db.Properties on E.PropertyID equals P.PropertyID
where P.Facilities.Contains(content)
select new
{
regdate = E.RegisterDate,
region = E.Region,
Estype = E.EstateType,
Fac = P.Facilities,
deal = P.DealType,
price = P.TotalCost,
img = E.Picture,
addrss = O.Address,
area = P.Area,
tel = P.TellNum,
bed = P.RoomNum,
park = P.ParikingNum
};
Repeater2.DataSource = query.OrderByDescending(x => x.regdate);
Repeater2.DataBind();
when user checked some checkbox "content" for example have this value:
SearchEstate.aspx?content=ContextB ContextE ContextJ
I Want search this values in Facility field in db
How can I do this? (Sorry for my bad English)
I have the feeling your are looking for something along the lines of this query:
var content = Request.QueryString["content"];
string[] contentArray = content.Split(' ');
//...
var query = //...
where P.Facilities.Any(f => contentArray.Contains(f.FacilityName))
//...
(or instead of FacilityName some other property of Facility)
But I am not sure.
I have an Oracle function that returns a record set.
I introduced parameters to the Oracle function and this is causing the front-end code to go haywire.
Here's my front-end code.
OracleCommand od = oc.CreateCommand();
od.CommandType = System.Data.CommandType.Text;
od.CommandText = " select * from table(pkg_fetchPOInfo.getPORowsTable(:1,:2))";
//od.CommandText = "pkg_fetchPOInfo.getPORowsTable";
//od.CommandType = System.Data.CommandType.TableDirect;
OracleParameter op1 = new OracleParameter();
op1.ParameterName = "1";
op1.OracleDbType = OracleDbType.Varchar2;
op1.Direction = System.Data.ParameterDirection.Input;
op1.Size = 6;
op1.Value = strPONumber;
od.Parameters.Add(op1);
OracleParameter op2 = new OracleParameter();
op2.ParameterName = "2";
op2.OracleDbType = OracleDbType.Varchar2;
op2.Direction = System.Data.ParameterDirection.Input;
op2.Size = 3;
op2.Value = "US";
od.Parameters.Add(op2);
If I execute the query in the front-end SQLPLUS, I get a recordset.
This code works if I remove the parameters from the package and the front-end code.
select * from table(pkg_fetchPOInfo.getPORowsTable('1007446','US')); --works in SQLPLUS.
select * from table(pkg_fetchPOInfo.getPORowsTable()); --works in both places.
Am I assigning the parameters incorrectly?
Package Definition:
CREATE OR REPLACE
PACKAGE TESTP AS
function TESTPIPE(nr in number, nr2 in number) return varchartabletype pipelined;
END TESTP;
CREATE OR REPLACE
PACKAGE BODY TESTP AS
function TESTPIPE(nr in number, nr2 in number) return varchartabletype pipelined AS
CURSOR TESTPIPE_cur
IS
SELECT (level + 1) datam
FROM dual
connect by level < nr;
vtt varchartabletype ;
BEGIN
OPEN TESTPIPE_cur;
LOOP
FETCH testpipe_cur
BULK COLLECT INTO vtt LIMIT nr2;
FOR indx IN 1 .. vtt.COUNT
LOOP
Pipe Row ( vtt( indx ) ) ;
END LOOP;
EXIT WHEN testpipe_cur%NOTFOUND;
END LOOP;
END TESTPIPE;
END TESTP;
.NET Code:
public static void pipeTest()
{
String conString = GetConnectionString();
OracleConnection _conn = new OracleConnection(conString);
_conn.Open();
OracleCommand oCmd = new OracleCommand();
oCmd.CommandText = "begin open :crs for Select * from table(testp.testpipe(:nr,:nr2)); end;";
oCmd.CommandType = CommandType.Text ;
oCmd.Connection = _conn;
OracleParameter crs = new OracleParameter();
crs.OracleDbType = OracleDbType.RefCursor;
crs.Direction = ParameterDirection.Output;
crs.ParameterName = "crs";
oCmd.Parameters.Add(crs);
OracleParameter nr = new OracleParameter();
nr.OracleDbType = OracleDbType.Int64;
nr.Direction = ParameterDirection.Input ;
nr.ParameterName = "nr";
nr.Value = 25;
oCmd.Parameters.Add(nr);
OracleParameter nr2 = new OracleParameter();
nr2.OracleDbType = OracleDbType.Int64;
nr2.Direction = ParameterDirection.Input;
nr2.ParameterName = "nr2";
nr2.Value = 10;
oCmd.Parameters.Add(nr2);
using (OracleDataReader MyReader = oCmd.ExecuteReader())
{
int ColumnCount = MyReader.FieldCount;
// get the data and add the row
while (MyReader.Read())
{
String s = MyReader.GetOracleValue(0).ToString();
Console.WriteLine(string.Format("i={0}", s));
}
}
Console.ReadLine();
}