Skip to content

Instantly share code, notes, and snippets.

View AdityaSoni19031997's full-sized avatar
🎯
Focusing

Aditya Soni AdityaSoni19031997

🎯
Focusing
View GitHub Profile
@AdityaSoni19031997
AdityaSoni19031997 / xor_in_Keras.py
Last active August 8, 2017 12:16
Comparing Keras And Tensorflow(XOR Function Implementation)
import numpy as np
from keras.models import Sequential
from keras.layers.core import Activation, Dense
training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
target_data = np.array([[0],[1],[1],[0]], "float32")
model = Sequential()
model.add(Dense(32, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
@AdityaSoni19031997
AdityaSoni19031997 / undirected_graph.py
Created August 14, 2017 06:05
Implementing Graph Algorithms (Basic Level in Python)
import Queue
class UndirectedGraph(object):
"""
Undirected Graph, with graph represented as an adjacency matrix
"""
def __init__(self, vertices):
self.adjacency_matrix = []
for i in range(vertices):
@AdityaSoni19031997
AdityaSoni19031997 / directed_graph.py
Last active August 14, 2017 06:36
Directed_Graph_Implementation_In_Python_Using_Adjacency_List
import Queue
class DirectedGraph(object):
"""
Directed Graph, with graph represented as an adjacency list
"""
def __init__(self):
self.adjacency_list = {}
@AdityaSoni19031997
AdityaSoni19031997 / nn_simplest.py
Created August 28, 2017 14:38
simplest Neural Net Possible
import numpy as np
X = np.array([ [0,0,1], [0,1,1], [1,0,1], [1,1,1] ])
y = np.array([ [0, 1, 1, 0] ])
w1 = np.random.random((3, 4)) - 0.5
w2 = np.random.random((4, 1)) - 0.5
for i in range(45000):
l1 = 1/(1+ np.exp(-(np.dot(X, w1))))
l2 = 1/(1+ np.exp(-(np.dot(l1,w2))))
delta_l2 = (y - l2) * (l2*(1-l2))
delta_l1 = delta_l2.dot(w2.T) * (l1*(1-l1))
@AdityaSoni19031997
AdityaSoni19031997 / shortcuts_in_C++_competitive_programming.cpp
Created September 6, 2017 11:17
Found IT Somewhere on the internet and added few lines which i use...
#include <bits/stdc++.h>
using namespace std;
#ifndef ONLINE_JUDGE
bool debug = false;
#else
bool debug = true;
#endif
@AdityaSoni19031997
AdityaSoni19031997 / license.txt
Last active September 19, 2018 03:31
sublime_text_build_3143_license_keys
## Sublime Text 3 Serial key build is 3176
The license key
----- BEGIN LICENSE -----
sgbteam
Single User License
EA7E-1153259
8891CBB9 F1513E4F 1A3405C1 A865D53F
115F202E 7B91AB2D 0D2A40ED 352B269B
@AdityaSoni19031997
AdityaSoni19031997 / kaggle.py
Last active November 16, 2022 15:14
Kaggle Helper Scripts
import seaborn as sns
from sklearn import preprocessing, ensemble
from scipy.stats import kendalltau
import pandas as pd
import random
#todo change module name
from tqdm import tqdm
import numpy as np
import pandas as pd
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
# This way we have randomness and are able to reproduce the behaviour within this cell.
np.random.seed(13)
def impact_coding(data, feature, target='y'):
'''
In this implementation we get the values and the dictionary as two different steps.
This is just because initially we were ignoring the dictionary as a result variable.
In this implementation the KFolds use shuffling. If you want reproducibility the cv
could be moved to a parameter.
# Importing dataset
dataset <- read.csv('ASD.csv')
# Taking care of missing data
# Fixing ethnicity
dataset[dataset == "?"] <- NA
summary(dataset)
#install.packages('DescTools')
library(DescTools)