PDD | Advanced Bloom Filter Based Algorithms for Efficient

by jparkie Java Version: 0.1.1 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | PDD Summary

PDD is a Java library typically used in Big Data, Spark applications. PDD has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, Maven.

Implementation of Advanced Bloom Filter Based Algorithms for Efficient Approximate Data De-Duplication in Streams as described by Suman K. Bera, Sourav Dutta, Ankur Narang, and Souvik Bhattacherjee. This library seeks to provide a production-oriented library for probabilistically de-duplicating unbounded data streams in real-time streaming scenarios (i.e. Storm, Spark, Flink, and Samza) while utilizing a fixed bound on memory. Accordingly, this library implements three novel Bloom Filter algorithms from the prior-mentioned paper all of which are shown to converge faster towards stability and to improve false-negative rates (FNR) by 2 to 300 times in comparison with Stable Bloom Filters.

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Support

PDD has a low active ecosystem.

It has 247 star(s) with 21 fork(s). There are 10 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 1378 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of PDD is 0.1.1

Quality

PDD has 0 bugs and 0 code smells.

Security

PDD has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

PDD code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PDD is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

PDD releases are available to install and integrate.

Deployable package is available in Maven.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed PDD and discovered the below as its top functions. This is intended to give you an instant insight into PDD implemented functionality, and help decide if they suit your requirements.

Classify element
Update the probability of a duplicate predicate
MurmurHashBytes unsafe bytes
Hash bytes by 32 bits
Classifies the given element
Update the reported duplicate probability
Set the hash buffer
Reset the bloom filter
Returns a 128 - bit hash code
Copies length bytes from src to dst
Create a new bloom filter array
Custom deserialization
Deserialization
Reads a bit array from a DataInputStream
Calculates the number of bits required to hold the given number of bits
Compares this object to another
Compares two BSBFSDDependency objects
Compares two BSBF duplicated objects
Adds all bits from another BitArray
Compare two BitArrays

Get all kandi verified functions for this library.

PDD Key Features

No Key Features are available at this moment for PDD.

PDD Examples and Code Snippets

No Code Snippets are available at this moment for PDD.

Community Discussions

Trending Discussions on PDD

Extract NetCDF Variable and create New NetCDF

Python+tkinter: Text widget doesn't change text immidiately

Using function on the whole list of XTS objects

In R, how to make the jitter (geom_jitter()) stay inside its correspondant boxplot without extending over the neighboring boxplots?

SQL join two tables by modifying on columns

Converting Multiple existing xts objects to multiple data.frames

query to get all not fully paid invoices

SqlSessionTemplate is not serializable in flink

I am trying to use CNN for stock price prediction but my code does not seem to work, what do I need to change or add?

Writing pandas dataframe appended text on the top

QUESTION

Extract NetCDF Variable and create New NetCDF

Asked 2022-Feb-26 at 15:37

I need some help with manipulating NetCDF files. In total I have 10 files for 10 years respectively. Each year hast multiple (the same) variables, some of them also covering daily values. Here, I show you one example for the structure:

...

ANSWER

Answered 2022-Feb-26 at 15:37

The NCO ncecat command, documented here, does exactly what you seem to want:

Source https://stackoverflow.com/questions/70953036

QUESTION

Python+tkinter: Text widget doesn't change text immidiately

Asked 2022-Jan-06 at 22:27

I have a tk.Text() widget and a button. When the button is clicked, I want to change the text in the Text widget, then conduct a lengthy job. Here is code snippet from the button command function:

...

ANSWER

Answered 2022-Jan-06 at 22:27

The problem here is that the window is not being updated after the text is inserted. This is because events are only processed after a callback returns.

To force the window to process the events before the callback returns, you can call self.root.update_idletasks() (at CoolCloud's suggestion, you shouldn't use update() unless it's absolutely necessary). Here is what the do_get() should look like:

Source https://stackoverflow.com/questions/70609391

QUESTION

Using function on the whole list of XTS objects

Asked 2021-Dec-14 at 18:17

I'm trying to analyze candlestick formation Marubozu in R. So far I was able to download the different stocks data and find the formation using "Candlesticks" library in one stock data. I would like to automate that process so that I can run the CSPMarubozu function on many stocks at the same time.

My main problem is that I cannot really understand how can I pass the list of data to this function. While trying to do it with for loop (Try 1) I get following error: "Error in CSPMarubozu((names(stocks_list[i])), n = 20, ATRFactor = 0.8, : Price series must contain Open, High, Low and Close." I know, that I can't pass the character variable to this function, but I can't find the way to get index names without the "" mark. (ex. "AMZN" and I need just AMZN"

My other try (Try 2) was to do it with lapply() function but the same problem occurs

Here is my code:

...

ANSWER

Answered 2021-Dec-14 at 18:17

This downloads stocks and then shows three different equivalent ways of processing each stock. We use dim(...) but that would be replaced with whatever processing is desired. Note that if x is an xts object for a stock having OHLC as well as adjusted close and volume then Op(x), Hi(x), Lo(x), Cl(x), Ad(x) and Vo(x) are the vectors of Open, High, Low, Close, Adjusted Close and Volume.

Although the code below seems preferable getSymbols(stocks); L <- mget(stocks) also works to put the stocks loose into your workspace and then collect them into a list L.

Source https://stackoverflow.com/questions/70350246

QUESTION

In R, how to make the jitter (geom_jitter()) stay inside its correspondant boxplot without extending over the neighboring boxplots?

Asked 2021-Dec-02 at 15:22

I would like to find a way for the jitter to stay in its own boxplot, without extending over the neighboring boxplots.

So far, I looked at this answers:

but none of them really addressed my issue; the main difference is that I have 3 groups running through a timeline on the X-axis.

The code I have so far:

...

ANSWER

Answered 2021-Dec-01 at 18:02

Specify the dodge width

+ geom_jitter(width = 0.05)

or geom_point(position = position_jitter(width = 0.05))

Source https://stackoverflow.com/questions/70188410

QUESTION

SQL join two tables by modifying on columns

Asked 2021-Sep-27 at 14:14

I have 3 tables on PostgreSQL:

SPENDINGS:

...

ANSWER

Answered 2021-Sep-27 at 14:14

try something as follows (you can use what ever join you would like to in place of inner)

Source https://stackoverflow.com/questions/69347334

QUESTION

Converting Multiple existing xts objects to multiple data.frames

Asked 2021-Sep-19 at 19:58

There is already a thread asking how to convert multiple xts objects into as many data.frames here. Unfortunately, the solutions show how to do it for data that is being downloaded in .GlobalEnv. Moreover, the first answer of mentioned thread suggests to create a new environment, download the objects into it, and transform everything inside with the following code: stocks <- eapply(dataEnv, as.data.frame).

However, this creates a large list stored in the variable stocks, whereas I need the objects to remain discrete. Even when I run the code without generating a list (i.e., by just applying eapply(dataEnv, as.data.frame)), nothing happens. This has been documented here. In order to update the original object, the answer to this question was to use a code that looks like this: NKLA <- fortify.zoo(NKLA). This solution, which by the way works, is ok for a few objects that can be done manually and I need to automatise the process.

In my case, the objects are already downloaded and some of the them are data.frames, some are xts objects, and there might even be other objects.

What I need is to find the xts objects and transform them into data.frames.

In order to find the xts objects, I use the following code: xtsObjects <- which(unlist(eapply(.GlobalEnv, is.xts))), but applying xtsObjects <- fortify.zoo(xtsObjects) only creates yet another object called xtsObjects that contains, for example, 2 obs. of 2 variables (because there are 2 xts objects in the environment).

For example, the following code (which should be reproducible) does not change the discrete xts objects into discrete data.frames:

...

ANSWER

Answered 2021-Sep-19 at 19:58

Use the names(which()) and then lapply.

Source https://stackoverflow.com/questions/69245999

QUESTION

query to get all not fully paid invoices

Asked 2021-Apr-30 at 21:33

I have these tables invoices,payments,payments_details, the invoices table have all the invoices that the user should pay created when a contract is created, this contract may have 1 invoice ore more, the payments table have all the payments for a contract (user may pay more one payment for each invoice) and the last table payments_details have the details for each payment in the payments table E.G. the payment may have deffirent payment methods such as cash, or cash and visa, or chash and visa and cheques. I'm getting payment value by getting the sum for payment method values from payments_details`, here is my tables script :

...

ANSWER

Answered 2021-Apr-30 at 21:33

Try this:

Source https://stackoverflow.com/questions/67339188

QUESTION

SqlSessionTemplate is not serializable in flink

Asked 2021-Mar-21 at 09:26

My flink aplication throws such exception when it starts:

...

ANSWER

Answered 2021-Mar-21 at 09:26

Rather than instantiating the Mapper object in the constructor, you can do this in the sink's open method, and then make the Mapper transient.

The sink's constructor is called on the Flink client, and the sink has to be serialized and sent to the task managers. Whereas the sink's open method is called once in each task manager as the job begins.

Source https://stackoverflow.com/questions/66729576

QUESTION

I am trying to use CNN for stock price prediction but my code does not seem to work, what do I need to change or add?

Asked 2021-Jan-28 at 05:40

import math
import numpy as np
import pandas as pd
import pandas_datareader as pdd
from sklearn.preprocessing import MinMaxScaler
from keras.layers import Dense, Dropout, Activation, LSTM, Convolution1D, MaxPooling1D, Flatten
from keras.models import Sequential
import matplotlib.pyplot as plt

df = pdd.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2020-12-31')
data = df.filter(['Close'])
dataset = data.values
len(dataset)

# 2265

training_data_size = math.ceil(len(dataset)*0.7)
training_data_size

# 1586

scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)
scaled_data

# array([[0.04288701],
#       [0.03870297],
#       [0.03786614],
#       ...,
#       [0.96610873],
#       [0.98608785],
#       [1.        ]])

train_data = scaled_data[0:training_data_size,:]
x_train = []
y_train = []
for i in range(60, len(train_data)):
    x_train.append(train_data[i-60:i, 0])
    y_train.append(train_data[i,0])
    if i<=60:
        print(x_train)
        print(y_train)  

'''
[array([0.04288701, 0.03870297, 0.03786614, 0.0319038 , 0.0329498 ,
       0.03577404, 0.03504182, 0.03608791, 0.03640171, 0.03493728,
       0.03661088, 0.03566949, 0.03650625, 0.03368202, 0.03368202,
       0.03598329, 0.04100416, 0.03953973, 0.04110879, 0.04320089,
       0.04089962, 0.03985353, 0.04037657, 0.03566949, 0.03640171,
       0.03619246, 0.03253139, 0.0294979 , 0.03033474, 0.02960253,
       0.03002095, 0.03284518, 0.03357739, 0.03410044, 0.03368202,
       0.03472803, 0.02803347, 0.02792885, 0.03556487, 0.03451886,
       0.0319038 , 0.03127613, 0.03274063, 0.02688284, 0.02635988,
       0.03211297, 0.03096233, 0.03472803, 0.03713392, 0.03451886,
       0.03441423, 0.03493728, 0.03587866, 0.0332636 , 0.03117158,
       0.02803347, 0.02897494, 0.03546024, 0.03786614, 0.0401674 ])]
[0.03933056376752886]
'''

x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_train.shape

# (1526, 60, 1)

model = Sequential()
model.add(Convolution1D(64, 3, input_shape= (100,4), padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Convolution1D(32, 3, padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(1))
model.add(Activation('linear'))
model.summary()

model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])
model.fit(X_train, y_train, batch_size=50, epochs=50, validation_data = (X_test, y_test), verbose=2)

test_data = scaled_data[training_data_size-60: , :]
x_test = []
y_test = dataset[training_data_size: , :]
for i in range(60, len(test_data)):
    x_test.append(test_data[i-60:i, 0])

x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
rsme = np.sqrt(np.mean((predictions - y_test)**2))
rsme

train = data[:training_data_size]
valid = data[training_data_size:]
valid['predictions'] = predictions
plt.figure(figsize=(16,8))
plt.title('PFE')
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price in $', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close', 'predictions']])
plt.legend(['Train', 'Val', 'predictions'], loc='lower right')
plt.show

import numpy as np

y_test, predictions = np.array(y_test), np.array(predictions)
mape = (np.mean(np.abs((predictions - y_test) / y_test))) * 100
accuracy = 100 - mape
print(accuracy)

...

ANSWER

Answered 2021-Jan-28 at 05:38

Your model doesn't tie to your data.

Change this line:

Source https://stackoverflow.com/questions/65931302

QUESTION

Writing pandas dataframe appended text on the top

Asked 2020-Oct-11 at 06:14

I have the pandas data frame. I want to write it in a text file and add text on the top of the frame.

e.g (this is the data.

...

ANSWER

Answered 2020-Oct-11 at 06:14

You can create a dataframe of the strings you want at the top and then append your main dataframe. Make sure the column names are the same before appending, so that it lines up (0 in my example):

Source https://stackoverflow.com/questions/64300984

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

Vulnerabilities

No vulnerabilities reported

Install PDD

You can download it from GitHub, Maven.
You can use PDD 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 PDD 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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