amansharma2910

import java.util.Scanner;
import java.lang.String;
import java.util.ArrayList;

class arrival{
    String name;
    int id;
    String address;
    String time;
    double fare;

amansharma2910

• Blockchain is a specific type of database.
• It differs from a typical database in the way it stores information; blockchains store data in blocks that are then chained together.
• As new data comes in it is entered into a fresh block. Once the block is filled with data it is chained onto the previous block, which makes the data chained together in chronological order.
• Different types of information can be stored on a blockchain but the most common use so far has been as a ledger for transactions.
• In Bitcoin’s case, blockchain is used in a decentralized way so that no single person or group has control—rather, all users collectively retain control.

amansharma2910

print("Hello world")

amansharma2910

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchblaze.mltests as mls

# creating an exemplary model class
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)

amansharma2910

# step-1: importing model class from sklearn
from sklearn.cluster import AgglomerativeClustering

# step-2: creating model class object; n_clusters denotes the number of clusters to find
model = AgglomerativeClustering(n_clusters = 3) 

# step-3: fitting training data and then predicting cluster index for each data instance
model.fit_predict(X)

amansharma2910

# step-1: importing model class from sklearn
from sklearn.cluster import DBSCAN

# step-2: creating model class object
# eps- max. distance between two samples to be considered neighborhs (default = 0.5)
# min_samples- The number of samples in a neighborhood for a point to be considered as a core point (default = 5)
model = DBSCAN(eps = 1.5, min_samples = 6) 

# step-3: fitting training data and then predicting cluster index for each data instance
model.fit_predict(X)

amansharma2910

# step-1: importing model class from sklearn
from sklearn.cluster import KMeans

# step-2: creating model class object; n_clusters denotes number of assumed clusters 
model = KMeans(n_clusters = 5) 

# step-3: fitting training data and then predicting cluster index for each data instance
model.fit_predict(X)

amansharma2910

# running inference for k=8
test_predictions = KNNClassifier(X_train, y_train, X_test, k = 8)

# checking the accuracy
acc = accuracy(y_test, test_predictions)
print('Model accuracy (k = 8) = ', acc*100)

amansharma2910

k_values = list(range(1,20))
accuracy_list = []
for k in k_values:
    test_predictions = KNNClassifier(X_train, y_train, X_test, k)
    accuracy_list.append(accuracy(y_test, test_predictions))
    
# plotting k-value vs accuracy plot
sns.barplot(k_values, accuracy_list)

amansharma2910

from sklearn.neighbors import KNeighborsClassifier as KNN

model = KNN()
model.fit(X_train, y_train)
preds = model.predict(X_test)

acc = accuracy(y_test, preds)
print('Model accuracy (Sklearn) = ', acc*100)