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View Quantitative Analysis of Apps on Google Playstore.ipynb
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SushilShrestha /
Last active Jan 31, 2019
Probability Mass Functions / Probability Density functions

PDF is defined by a Probability Distribution curve. PDF can be thought of like, given a sample data points, the PDF squashes the points to get a model (curve or distribution curve) that can be used to define the total samples. We can select a pdf train it on our dataset. find the pdf parameters and then use the model to predict future events. So PDF is a function p(x) that returns probability of x and uses parameters we obtained from the population.

We have freedom to choose what to use for p(x). There are multiple density functions you can choose from.

  • Bernoulli's Distribution
  • Poission's Distribution
  • Normal Distribution
  • Bionomial Distribution
  • Negative Binomial Distribution
View NepaliEvents.ics
SUMMARY:सरस्वती पूजा (शिक्षणसंस्
था बिदा)
SUMMARY:विजया दशमी
View activity

Activity Scheduling (Dynamic approach)

The table we are going to build for this activity scheduling problem will track the number of activities we can schedule, given a list of activities sorted by finish time. We will try to maximize the number of activities we can schedule. Consider a following activities that we need to choose from. ![table][tablephoto]

We want to solve it using the dynamic approach so we create a table of (n+1) X (n+1) to store our data for the dynamic programming. Each item say c[i][j] means number of activities that can be scheduled in between activity[i] and activity[j].

Following table is the final table we get, if we use the dynamic programming to solve the above problem.


SushilShrestha /
Last active Aug 25, 2017
PP and auto size tile photo using imagemagick for a4 size printing


ppauto inputfilename




Input File

Input File

Output File

View react-native colorful console android

For Linux

react-native log-android |
sed -e 's/\(.*action @ .*\)/\o033[32m\1\o033[39m/' \
    -e 's/\(.* —— log end.*\)/\o033[32m\1\o033[39m/' \
    -e 's/\(.*prev state.*\)/\o033[36m\1\o033[39m/' \
    -e 's/\(.*c action.*\)/\o033[36m\1\o033[39m/' \
    -e 's/\(.*\%c next state.*\)/\o033[36m\1\o033[39m/' \
    -e 's/\(.*W ReactNativeJS\:.*\)/\o033[33m\1\o033[39m/' \
    -e 's/\(.*E ReactNativeJS\:.*\)/\o033[31m\1\o033[39m/'
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