Let’s Build a Svelte App

Today we are going to develop the Game Detail page, that will show how retrocompatible a game is and it will allow the users to share their experiences with the game.

Last week we developed the game list, both the backend and frontend, that will show all the games according to the user’s query. If you want to read it, I will be waiting for you.

For this week, we will develop the game detail page. This page will show the user’s votes and comments about a game’s retrocompatibility, and if the game is not locked, it will allow these users to share their experiences.

If you want to jump straight to the code, you can find it here.

Game Detail Page

To create the backend route we follow the same procedure as for the list: we create a file with the name of the route, in this case, [id].json.ts in /game . [id] will be substituted dynamically for the game’s id. .json is important because without it, Sapper would not be able to distinguish between the frontend /game/[id].svelte route and the backend /game/[id].ts route. With our ‘route’ set, we declare the GET function and test it.

export async function get(req: Request, res: Response, next: NextFunction) {
  const { id } = req.params;
  const game = await models.Game.findById(id);
  if (!game) {
    return res.status(404).send({
      message: 'Game Not Found!'
    });
  }
  return res.send(game);
}

#javascript #typescript #svelte

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Buddha Community

A Practical Guide of Unsupervised Learning Algorithms

In this article, learn about Machine Learning Tutorial: A Practical  Guide of Unsupervised Learning Algorithms. Machine learning is a fast-growing technology that allows computers to learn from the past and predict the future. It uses numerous algorithms for building mathematical models and predicting future trends. Machine learning (ML) has widespread applications in the industry, including speech recognition, image recognition, churn prediction, email filtering, chatbot development, recommender systems, and much more.

Machine learning (ML) can be classified into three main categories; supervised, unsupervised, and reinforcement learning. In supervised learning, the model is trained on labeled data. While in unsupervised learning, unlabeled data is provided to the model to predict the outcomes. Reinforcement learning is feedback learning in which the agent collects a reward for each correct action and gets a penalty for a wrong decision. The goal of the learning agent is to get maximum reward points and deduce the error.

What is Unsupervised Learning?

In unsupervised learning, the model learns from unlabeled data without proper supervision.

Unsupervised learning uses machine learning techniques to cluster unlabeled data based on similarities and differences. The unsupervised algorithms discover hidden patterns in data without human supervision. Unsupervised learning aims to arrange the raw data into new features or groups together with similar patterns of data.

For instance, to predict the churn rate, we provide unlabeled data to our model for prediction. There is no information given that customers have churned or not. The model will analyze the data and find hidden patterns to categorize into two clusters: churned and non-churned customers.

Unsupervised Learning Approaches

Unsupervised algorithms can be used for three tasks—clustering, dimensionality reduction, and association. Below, we will highlight some commonly used clustering and association algorithms.

Clustering Techniques

Clustering, or cluster analysis, is a popular data mining technique for unsupervised learning. The clustering approach works to group non-labeled data based on similarities and differences. Unlike supervised learning, clustering algorithms discover natural groupings in data. 

A good clustering method produces high-quality clusters having high intra-class similarity (similar data within a cluster) and less intra-class similarity (cluster data is dissimilar to other clusters). 

It can be defined as the grouping of data points into various clusters containing similar data points. The same objects remain in the group that has fewer similarities with other groups. Here, we will discuss two popular clustering techniques: K-Means clustering and DBScan Clustering.

K-Means Clustering

K-Means is the simplest unsupervised technique used to solve clustering problems. It groups the unlabeled data into various clusters. The K value defines the number of clusters you need to tell the system how many to create.

K-Means is a centroid-based algorithm in which each cluster is associated with the centroid. The goal is to minimize the sum of the distances between the data points and their corresponding clusters.

It is an iterative approach that breaks down the unlabeled data into different clusters so that each data point belongs to a group with similar characteristics.

K-means clustering performs two tasks:

1. Using an iterative process to create the best value of K.
2. Each data point is assigned to its closest k-center. The data point that is closer to the particular k-center makes a cluster.

 

An illustration of K-means clustering. Image source

DBScan Clustering

“DBScan” stands for “Density-based spatial clustering of applications with noise.” There are three main words in DBscan: density, clustering, and noise. Therefore, this algorithm uses the notion of density-based clustering to form clusters and detect the noise.

Clusters are usually dense regions that are separated by lower density regions. Unlike the k-means algorithm, which works only on well-separated clusters, DBscan has a wider scope and can create clusters within the cluster. It discovers clusters of various shapes and sizes from a large set of data, which consists of noise and outliers.

There are two parameters in the DBScan algorithm:

minPts: The threshold, or the minimum number of points grouped together for a region considered as a dense region.

eps(ε): The distance measure used to locate the points in the neighborhood. 

 

An illustration of density-based clustering. Image Source 

Association Rule Mining

An association rule mining is a popular data mining technique. It finds interesting correlations in large numbers of data items. This rule shows how frequently items occur in a transaction.

Market Basket Analysis is a typical example of an association rule mining that finds relationships between items in the grocery store. It enables retailers to identify and analyze the associations between items that people frequently buy.

Important terminology used in association rules:

Support: It tells us about the combination of items bought frequently or frequently bought items.

Confidence: It tells us how often the items A and B occur together, given the number of times A occurs.

Lift: The lift indicates the strength of a rule over the random occurrence of A and B. For instance, A->B, the life value is 5. It means that if you buy A, the occurrence of buying B is five times.

The Apriori algorithm is a well-known association rule based technique.

Apriori algorithm 

The Apriori algorithm was proposed by R. Agarwal and R. Srikant in 1994 to find the frequent items in the dataset. The algorithm’s name is based on the fact that it uses prior knowledge of frequently occurring things. 

The Apriori algorithm finds frequently occurring items with minimum support. 

It consists of two steps:

• Generation of candidate itemsets.
• Removing items that are infrequent and don’t fulfill the criteria of minimum support.

Practical Implementation of Unsupervised Algorithms 

In this tutorial, you will learn about the implementation of various unsupervised algorithms in Python. Scikit-learn is a powerful Python library widely used for various unsupervised learning tasks. It is an open-source library that provides numerous robust algorithms, which include classification, dimensionality reduction, clustering techniques, and association rules.

Let’s begin!

1. K-Means algorithm 

Now let’s dive deep into the implementation of the K-Means algorithm in Python. We’ll break down each code snippet so that you can understand it easily.

Import libraries

First of all, we will import the required libraries and get access to the functions.

#Let's import the required libraries
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns

Loading the dataset 

The dataset is taken from the kaggle website. You can easily download it from the given link. To load the dataset, we use the pd.read_csv() function. head() returns the first five rows of the dataset.

my_data = pd.read_csv('Customers_Mall.csv.') my_data.head()

The dataset contains five columns: customer ID, gender, age, annual income in (K$), and spending score from 1-100. 

Data Preprocessing 

The info() function is used to get quick information about the dataset. It shows the number of entries, columns, total non-null values, memory usage, and datatypes. 

my_data.info()

 

To check the missing values in the dataset, we use isnull().sum(), which returns the total number of null values.

 

#Check missing values 
my_data.isnull().sum()

 

The box plot or whisker plot is used to detect outliers in the dataset. It also shows a statistical five number summary, which includes the minimum, first quartile, median (2nd quartile), third quartile, and maximum.

my_data.boxplot(figsize=(8,4))

Using Box Plot, we’ve detected an outlier in the annual income column. Now we will try to remove it before training our model. 

#let's remove outlier from data
med =61
my_data["Annual Income (k$)"] = np.where(my_data["Annual Income (k$)"] >
 120,med,my_data['Annual Income (k$)'])

The outlier in the annual income column has been removed now to confirm we used the box plot again.

my_data.boxplot(figsize=(8,5))

Data Visualization

A histogram is used to illustrate the important features of the distribution of data. The hist() function is used to show the distribution of data in each numerical column.

my_data.hist(figsize=(6,6)) 

The correlation heatmap is used to find the potential relationships between variables in the data and to display the strength of those relationships. To display the heatmap, we have used the seaborn plotting library.

plt.figure(figsize=(10,6)) sns.heatmap(my_data.corr(), annot=True, cmap='icefire').set_title('seaborn') plt.show()

Choosing the Best K Value

The iloc() function is used to select a particular cell of the data. It enables us to select a value that belongs to a specific row or column. Here, we’ve chosen the annual income and spending score columns.

 

X_val = my_data.iloc[:, 3:].values X_val

 

# Loading Kmeans Library

from sklearn.cluster import KMeans

Now we will select the best value for K using the Elbow’s method. It is used to determine the optimal number of clusters in K-means clustering.

my_val = []

for i in range(1,11):

    kmeans = KMeans(n_clusters = i, init='k-means++', random_state = 123)

    kmeans.fit(X_val)

    my_val.append(kmeans.inertia_)

The sklearn.cluster.KMeans() is used to choose the number of clusters along with the initialization of other parameters. To display the result, just call the variable.

my_val #Visualization of clusters using elbow’s method plt.plot(range(1,11),my_val) plt.xlabel('The No of clusters') plt.ylabel('Outcome') plt.title('The Elbow Method') plt.show()

Through Elbow’s Method, when the graph looks like an arm, then the elbow on the arm is the best value of K. In this case, we’ve taken K=3, which is the optimal value for K.

kmeans = KMeans(n_clusters = 3, init='k-means++') kmeans.fit(X_val) #To show centroids of clusters  kmeans.cluster_centers_ #Prediction of K-Means clustering  y_kmeans = kmeans.fit_predict(X_val) y_kmeans

Splitting the dataset into three clusters

The scatter graph is used to plot the classification results of our dataset into three clusters.

plt.scatter(X_val[y_kmeans == 0,0], X_val[y_kmeans == 0,1], c='red',s=100)

plt.scatter(X_val[y_kmeans == 1,0], X_val[y_kmeans == 1,1], c='green',s=100)

plt.scatter(X_val[y_kmeans == 2,0], X_val[y_kmeans == 2,1], c='orange',s=100)

plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300, c='brown')

plt.title('K-Means Unsupervised Learning')

plt.show()

2. Apriori Algorithm

To implement the apriori algorithm, we will utilize “The Bread Basket” dataset. The dataset is available on Kaggle and you can download it from the link. This algorithm suggests products based on the user’s purchase history. Walmart has greatly utilized the algorithm to recommend relevant items to its users.

Let’s implement the Apriori algorithm in Python. 

Import libraries 

To implement the algorithm, we need to import some important libraries.

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

import seaborn as sns

Loading the dataset 

The dataset contains five columns and 20507 entries. The data_time is a prominent column and we can extract many vital insights from it.

my_data= pd.read_csv("bread basket.csv") my_data.head()

Data Preprocessing 

Convert the data_time into an appropriate format.

my_data['date_time'] = pd.to_datetime(my_data['date_time'])

#Total No of unique customers

my_data['Transaction'].nunique()

Now we want to extract new columns from the data_time to extract meaningful information from the data.

#Let's extract date

my_data['date'] = my_data['date_time'].dt.date

#Let's extract time

my_data['time'] = my_data['date_time'].dt.time

#Extract month and replacing it with String

my_data['month'] = my_data['date_time'].dt.month

my_data['month'] = my_data['month'].replace((1,2,3,4,5,6,7,8,9,10,11,12), 

                                          ('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug',

                                          'Sep','Oct','Nov','Dec'))

#Extract hour

my_data[‘hour’] = my_data[‘date_time’].dt.hour

# Replacing hours with text

# Replacing hours with text

hr_num = (1,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23)

hr_obj = (‘1-2′,’7-8′,’8-9′,’9-10′,’10-11′,’11-12′,’12-13′,’13-14′,’14-15’,

               ’15-16′,’16-17′,’17-18′,’18-19′,’19-20′,’20-21′,’21-22′,’22-23′,’23-24′)

my_data[‘hour’] = my_data[‘hour’].replace(hr_num, hr_obj)

# Extracting weekday and replacing it with String 

my_data[‘weekday’] = my_data[‘date_time’].dt.weekday

my_data[‘weekday’] = my_data[‘weekday’].replace((0,1,2,3,4,5,6), 

                                          (‘Mon’,’Tues’,’Wed’,’Thur’,’Fri’,’Sat’,’Sun’))

#Now drop date_time column

my_data.drop(‘date_time’, axis = 1, inplace = True)

After extracting the date, time, month, and hour columns, we dropped the data_time column.

Now to display, we simply use the head() function to see the changes in the dataset.

my_data.head()

# cleaning the item column

my_data[‘Item’] = my_data[‘Item’].str.strip()

my_data[‘Item’] = my_data[‘Item’].str.lower()

my_data.head()

Data Visualization 

To display the top 10 items purchased by customers, we used a barplot() of the seaborn library. 

plt.figure(figsize=(10,5))

sns.barplot(x=my_data.Item.value_counts().head(10).index, y=my_data.Item.value_counts().head(10).values,palette='RdYlGn')

plt.xlabel('No of Items', size = 17)

plt.xticks(rotation=45)

plt.ylabel('Total Items', size = 18)

plt.title('Top 10 Items purchased', color = 'blue', size = 23)

plt.show()

From the graph, coffee is the top item purchased by the customers, followed by bread.

Now, to display the number of orders received each month, the groupby() function is used along with barplot() to visually show the results.

mon_Tran =my_data.groupby('month')['Transaction'].count().reset_index() mon_Tran.loc[:,"mon_order"] =[4,8,12,2,1,7,6,3,5,11,10,9] mon_Tran.sort_values("mon_order",inplace=True) plt.figure(figsize=(12,5)) sns.barplot(data = mon_Tran, x = "month", y = "Transaction") plt.xlabel('Months', size = 14) plt.ylabel('Monthly Orders', size = 14) plt.title('No of orders received each month', color = 'blue', size = 18) plt.show()

To show the number of orders received each day, we applied groupby() to the weekday column.

wk_Tran = my_data.groupby('weekday')['Transaction'].count().reset_index()

wk_Tran.loc[:,"wk_ord"] = [4,0,5,6,3,1,2]

wk_Tran.sort_values("wk_ord",inplace=True)

plt.figure(figsize=(11,4))

sns.barplot(data = wk_Tran, x = "weekday", y = "Transaction",palette='RdYlGn')

plt.xlabel('Week Day', size = 14)

plt.ylabel('Per day orders', size = 14)

plt.title('Orders received per day', color = 'blue', size = 18)

plt.show()

 Implementation of the Apriori Algorithm 

We import the mlxtend library to implement the association rules and count the number of items.

from mlxtend.frequent_patterns import association_rules, apriori

tran_str= my_data.groupby(['Transaction', 'Item'])['Item'].count().reset_index(name ='Count')

tran_str.head(8)

Now we’ll make a mxn matrix where m=transaction and n=items, and each row represents whether the item was in the transaction or not.

Mar_baskt = tran_str.pivot_table(index='Transaction', columns='Item', values='Count', aggfunc='sum').fillna(0)

Mar_baskt.head()

We want to make a function that returns 0 and 1. 0 means that the item wasn’t present in the transaction, while 1 means the item exists.

def encode(val):

    if val<=0:

        return 0

    if val>=1:

        return 1

#Let's apply the function to the dataset

Basket=Mar_baskt.applymap(encode)

Basket.head()

#using apriori algorithm to set min_support 0.01 means 1% freq_items = apriori(Basket, min_support = 0.01,use_colnames = True) freq_items.head()

Using the association_rules() function to generate the most frequent items from the dataset.

App_rule= association_rules(freq_items, metric = "lift", min_threshold = 1) App_rule.sort_values('confidence', ascending = False, inplace = True) App_rule.head()

From the above implementation, the most frequent items are coffee and toast, both having a lift value of 1.47 and a confidence value of 0.70. 

3. Principal Component Analysis 

Principal component analysis (PCA) is one of the most widely used unsupervised learning techniques. It can be used for various tasks, including dimensionality reduction, information compression, exploratory data analysis and Data de-noising.

Let’s use the PCA algorithm!

First we import the required libraries to implement this algorithm.

import numpy as np 

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

from sklearn.decomposition import PCA

from sklearn.datasets import load_digits

Loading the Dataset 

To implement the PCA algorithm the load_digits dataset of Scikit-learn is used which can easily be loaded using the below command. The dataset contains images data which include 1797 entries and 64 columns.

 

#Load the dataset

my_data= load_digits()

#Creating features

X_value = my_data.data

#Creating target

#Let's check the shape of X_value

X_value.shape

 

 

 

#Each image is 8x8 pixels therefore 64px  my_data.images[10] #Let's display the image plt.gray()  plt.matshow(my_data.images[34])  plt.show()

Now let’s project data from 64 columns to 16 to show how 16 dimensions classify the data.

X_val = my_data.data 

y_val = my_data.target

my_pca = PCA(16)  

X_projection = my_pca.fit_transform(X_val)

print(X_val.shape)

print(X_projection.shape)

Using colormap we visualize that with only ten dimensions  we can classify the data points. Now we’ll select the optimal number of dimensions (principal components) by which data can be reduced into lower dimensions.

plt.scatter(X_projection[:, 0], X_projection[:, 1], c=y_val, edgecolor='white',

            cmap=plt.cm.get_cmap("gist_heat",12))

plt.colorbar();

pca=PCA().fit(X_val)

plt.plot(np.cumsum(my_pca.explained_variance_ratio_))

plt.xlabel('Principal components')

plt.ylabel('Explained variance')

Based on the below graph, only 12 components are required to explain more than 80% of the variance which is still better than computing all the 64 features. Thus, we’ve reduced the large number of dimensions into 12 dimensions to avoid the dimensionality curse. pca=PCA().fit(X_val)

plt.plot(np.cumsum(pca.explained_variance_ratio_))

plt.xlabel('Principal components')

plt.ylabel('Explained variance')



#Let's visualize how it looks like

Unsupervised_pca = PCA(12)  

X_pro = Unsupervised_pca.fit_transform(X_val)

print("New Data Shape is =>",X_pro.shape)

#Let's Create a scatter plot

plt.scatter(X_pro[:, 0], X_pro[:, 1], c=y_val, edgecolor='white',

            cmap=plt.cm.get_cmap("nipy_spectral",10))

plt.colorbar();

Wrapping Up 

In this machine learning tutorial, we’ve implemented the Kmeans, Apriori, and PCA algorithms. These are some of the most widely used algorithms, having numerous industrial applications and solve many real world problems. For instance, K-means clustering is used in astronomy to study stellar and galaxy spectra, solar polarization spectra, and X-ray spectra. And, Apriori is used by retail stores to optimize their product inventory. 

Dreaming of becoming a data scientist or data analyst even without a university and a college degree? Do you need the knowledge of data science and analysis for promotions in your current role?

Are you interested in securing your dream job in data science and analysis and looking for a way to get started, we can help you? With over 10 years of experience in data science and data analysis, we will teach you the rubrics, guiding you with one-on-one lessons from the fundamentals until you become a pro.

Our courses are affordable and easy to understand with numerous exercises and assignments you can learn from. At the completion of our courses, you’ll be readily equipped with technical and practical skills to take on any data science and data analysis role in companies, collaborate effectively among teams and help businesses meet and exceed their objectives by extracting actionable insights from data.

Original article sourced at: https://thedatascientist.com

#machine-learning 

How long does it take to develop/build an app?

With more of us using smartphones, the popularity of mobile applications has exploded. In the digital era, the number of people looking for products and services online is growing rapidly. Smartphone owners look for mobile applications that give them quick access to companies’ products and services. As a result, mobile apps provide customers with a lot of benefits in just one device.

Likewise, companies use mobile apps to increase customer loyalty and improve their services. Mobile Developers are in high demand as companies use apps not only to create brand awareness but also to gather information. For that reason, mobile apps are used as tools to collect valuable data from customers to help companies improve their offer.

There are many types of mobile applications, each with its own advantages. For example, native apps perform better, while web apps don’t need to be customized for the platform or operating system (OS). Likewise, hybrid apps provide users with comfortable user experience. However, you may be wondering how long it takes to develop an app.

To give you an idea of how long the app development process takes, here’s a short guide.

App Idea & Research

_Average time spent: two to five weeks _

This is the initial stage and a crucial step in setting the project in the right direction. In this stage, you brainstorm ideas and select the best one. Apart from that, you’ll need to do some research to see if your idea is viable. Remember that coming up with an idea is easy; the hard part is to make it a reality.

All your ideas may seem viable, but you still have to run some tests to keep it as real as possible. For that reason, when Web Developers are building a web app, they analyze the available ideas to see which one is the best match for the targeted audience.

Targeting the right audience is crucial when you are developing an app. It saves time when shaping the app in the right direction as you have a clear set of objectives. Likewise, analyzing how the app affects the market is essential. During the research process, App Developers must gather information about potential competitors and threats. This helps the app owners develop strategies to tackle difficulties that come up after the launch.

The research process can take several weeks, but it determines how successful your app can be. For that reason, you must take your time to know all the weaknesses and strengths of the competitors, possible app strategies, and targeted audience.

The outcomes of this stage are app prototypes and the minimum feasible product.

#android app #frontend #ios app #minimum viable product (mvp) #mobile app development #web development #android app development #app development #app development for ios and android #app development process #ios and android app development #ios app development #stages in app development

How to start an electric scooter facility/fleet in a university campus/IT park

Are you leading an organization that has a large campus, e.g., a large university? You are probably thinking of introducing an electric scooter/bicycle fleet on the campus, and why wouldn’t you?

Introducing micro-mobility in your campus with the help of such a fleet would help the people on the campus significantly. People would save money since they don’t need to use a car for a short distance. Your campus will see a drastic reduction in congestion, moreover, its carbon footprint will reduce.

Micro-mobility is relatively new though and you would need help. You would need to select an appropriate fleet of vehicles. The people on your campus would need to find electric scooters or electric bikes for commuting, and you need to provide a solution for this.

To be more specific, you need a short-term electric bike rental app. With such an app, you will be able to easily offer micro-mobility to the people on the campus. We at Devathon have built Autorent exactly for this.

What does Autorent do and how can it help you? How does it enable you to introduce micro-mobility on your campus? We explain these in this article, however, we will touch upon a few basics first.

Micro-mobility: What it is

You are probably thinking about micro-mobility relatively recently, aren’t you? A few relevant insights about it could help you to better appreciate its importance.

Micro-mobility is a new trend in transportation, and it uses vehicles that are considerably smaller than cars. Electric scooters (e-scooters) and electric bikes (e-bikes) are the most popular forms of micro-mobility, however, there are also e-unicycles and e-skateboards.

You might have already seen e-scooters, which are kick scooters that come with a motor. Thanks to its motor, an e-scooter can achieve a speed of up to 20 km/h. On the other hand, e-bikes are popular in China and Japan, and they come with a motor, and you can reach a speed of 40 km/h.

You obviously can’t use these vehicles for very long commutes, however, what if you need to travel a short distance? Even if you have a reasonable public transport facility in the city, it might not cover the route you need to take. Take the example of a large university campus. Such a campus is often at a considerable distance from the central business district of the city where it’s located. While public transport facilities may serve the central business district, they wouldn’t serve this large campus. Currently, many people drive their cars even for short distances.

As you know, that brings its own set of challenges. Vehicular traffic adds significantly to pollution, moreover, finding a parking spot can be hard in crowded urban districts.

Well, you can reduce your carbon footprint if you use an electric car. However, electric cars are still new, and many countries are still building the necessary infrastructure for them. Your large campus might not have the necessary infrastructure for them either. Presently, electric cars don’t represent a viable option in most geographies.

As a result, you need to buy and maintain a car even if your commute is short. In addition to dealing with parking problems, you need to spend significantly on your car.

All of these factors have combined to make people sit up and think seriously about cars. Many people are now seriously considering whether a car is really the best option even if they have to commute only a short distance.

This is where micro-mobility enters the picture. When you commute a short distance regularly, e-scooters or e-bikes are viable options. You limit your carbon footprints and you cut costs!

Businesses have seen this shift in thinking, and e-scooter companies like Lime and Bird have entered this field in a big way. They let you rent e-scooters by the minute. On the other hand, start-ups like Jump and Lyft have entered the e-bike market.

Think of your campus now! The people there might need to travel short distances within the campus, and e-scooters can really help them.

How micro-mobility can benefit you

What advantages can you get from micro-mobility? Let’s take a deeper look into this question.

Micro-mobility can offer several advantages to the people on your campus, e.g.:

• Affordability: Shared e-scooters are cheaper than other mass transportation options. Remember that the people on your campus will use them on a shared basis, and they will pay for their short commutes only. Well, depending on your operating model, you might even let them use shared e-scooters or e-bikes for free!
• Convenience: Users don’t need to worry about finding parking spots for shared e-scooters since these are small. They can easily travel from point A to point B on your campus with the help of these e-scooters.
• Environmentally sustainable: Shared e-scooters reduce the carbon footprint, moreover, they decongest the roads. Statistics from the pilot programs in cities like Portland and Denver showimpressive gains around this key aspect.
• Safety: This one’s obvious, isn’t it? When people on your campus use small e-scooters or e-bikes instead of cars, the problem of overspeeding will disappear. you will see fewer accidents.

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Best Electric Bikes and Scooters for Rental Business or Campus Facility

The electric scooter revolution has caught on super-fast taking many cities across the globe by storm. eScooters, a renovated version of old-school scooters now turned into electric vehicles are an environmentally friendly solution to current on-demand commute problems. They work on engines, like cars, enabling short traveling distances without hassle. The result is that these groundbreaking electric machines can now provide faster transport for less — cheaper than Uber and faster than Metro.

Since they are durable, fast, easy to operate and maintain, and are more convenient to park compared to four-wheelers, the eScooters trend has and continues to spike interest as a promising growth area. Several companies and universities are increasingly setting up shop to provide eScooter services realizing a would-be profitable business model and a ready customer base that is university students or residents in need of faster and cheap travel going about their business in school, town, and other surrounding areas.

Electric Scooters Trends and Statistics

In many countries including the U.S., Canada, Mexico, U.K., Germany, France, China, Japan, India, Brazil and Mexico and more, a growing number of eScooter users both locals and tourists can now be seen effortlessly passing lines of drivers stuck in the endless and unmoving traffic.

A recent report by McKinsey revealed that the E-Scooter industry will be worth― $200 billion to $300 billion in the United States, $100 billion to $150 billion in Europe, and $30 billion to $50 billion in China in 2030. The e-Scooter revenue model will also spike and is projected to rise by more than 20% amounting to approximately $5 billion.

And, with a necessity to move people away from high carbon prints, traffic and congestion issues brought about by car-centric transport systems in cities, more and more city planners are developing more bike/scooter lanes and adopting zero-emission plans. This is the force behind the booming electric scooter market and the numbers will only go higher and higher.

Companies that have taken advantage of the growing eScooter trend develop an appthat allows them to provide efficient eScooter services. Such an app enables them to be able to locate bike pick-up and drop points through fully integrated google maps.

List of Best Electric Bikes for Rental Business or Campus Facility 2020:

It’s clear that e scooters will increasingly become more common and the e-scooter business model will continue to grab the attention of manufacturers, investors, entrepreneurs. All this should go ahead with a quest to know what are some of the best electric bikes in the market especially for anyone who would want to get started in the electric bikes/scooters rental business.

We have done a comprehensive list of the best electric bikes! Each bike has been reviewed in depth and includes a full list of specs and a photo.

Billy eBike

 https://www.kickstarter.com/projects/enkicycles/billy-were-redefining-joyrides

To start us off is the Billy eBike, a powerful go-anywhere urban electric bike that’s specially designed to offer an exciting ride like no other whether you want to ride to the grocery store, cafe, work or school. The Billy eBike comes in 4 color options – Billy Blue, Polished aluminium, Artic white, and Stealth black.

Price: $2490

Available countries

Available in the USA, Europe, Asia, South Africa and Australia.This item ships from the USA. Buyers are therefore responsible for any taxes and/or customs duties incurred once it arrives in your country.

Features

• Control – Ride with confidence with our ultra-wide BMX bars and a hyper-responsive twist throttle.
• Stealth- Ride like a ninja with our Gates carbon drive that’s as smooth as butter and maintenance-free.
• Drive – Ride further with our high torque fat bike motor, giving a better climbing performance.
• Accelerate – Ride quicker with our 20-inch lightweight cutout rims for improved acceleration.
• Customize – Ride your own way with 5 levels of power control. Each level determines power and speed.
• Flickable – Ride harder with our BMX /MotoX inspired geometry and lightweight aluminum package

Specifications

• Maximum speed: 20 mph (32 km/h)
• Range per charge: 41 miles (66 km)
• Maximum Power: 500W
• Motor type: Fat Bike Motor: Bafang RM G060.500.DC
• Load capacity: 300lbs (136kg)
• Battery type: 13.6Ah Samsung lithium-ion,
• Battery capacity: On/off-bike charging available
• Weight: w/o batt. 48.5lbs (22kg), w/ batt. 54lbs (24.5kg)
• Front Suspension: Fully adjustable air shock, preload/compression damping /lockout
• Rear Suspension: spring, preload adjustment
• Built-in GPS

Why Should You Buy This?

• Riding fun and excitement
• Better climbing ability and faster acceleration.
• Ride with confidence
• Billy folds for convenient storage and transportation.
• Shorty levers connect to disc brakes ensuring you stop on a dime
• belt drives are maintenance-free and clean (no oil or lubrication needed)

**Who Should Ride Billy? **

Both new and experienced riders

**Where to Buy? **Local distributors or ships from the USA.

Genze 200 series e-Bike

 https://www.genze.com/fleet/

Featuring a sleek and lightweight aluminum frame design, the 200-Series ebike takes your riding experience to greater heights. Available in both black and white this ebike comes with a connected app, which allows you to plan activities, map distances and routes while also allowing connections with fellow riders.

Price: $2099.00

Available countries

The Genze 200 series e-Bike is available at GenZe retail locations across the U.S or online via GenZe.com website. Customers from outside the US can ship the product while incurring the relevant charges.

Features

• 2 Frame Options
• 2 Sizes
• Integrated/Removable Battery
• Throttle and Pedal Assist Ride Modes
• Integrated LCD Display
• Connected App
• 24 month warranty
• GPS navigation
• Bluetooth connectivity

Specifications

• Maximum speed: 20 mph with throttle
• Range per charge: 15-18 miles w/ throttle and 30-50 miles w/ pedal assist
• Charging time: 3.5 hours
• Motor type: Brushless Rear Hub Motor
• Gears: Microshift Thumb Shifter
• Battery type: Removable Samsung 36V, 9.6AH Li-Ion battery pack
• Battery capacity: 36V and 350 Wh
• Weight: 46 pounds
• Derailleur: 8-speed Shimano
• Brakes: Dual classic
• Wheels: 26 x 20 inches
• Frame: 16, and 18 inches
• Operating Mode: Analog mode 5 levels of Pedal Assist Thrott­le Mode

Norco from eBikestore

 https://ebikestore.com/shop/norco-vlt-s2/

The Norco VLT S2 is a front suspension e-Bike with solid components alongside the reliable Bosch Performance Line Power systems that offer precise pedal assistance during any riding situation.

Price: $2,699.00

Available countries

This item is available via the various Norco bikes international distributors.

Features

• VLT aluminum frame- for stiffness and wheel security.
• Bosch e-bike system – for their reliability and performance.
• E-bike components – for added durability.
• Hydraulic disc brakes – offer riders more stopping power for safety and control at higher speeds.
• Practical design features – to add convenience and versatility.

Specifications

• Maximum speed: KMC X9 9spd
• Motor type: Bosch Active Line
• Gears: Shimano Altus RD-M2000, SGS, 9 Speed
• Battery type: Power Pack 400
• Battery capacity: 396Wh
• Suspension: SR Suntour suspension fork
• Frame: Norco VLT, Aluminum, 12x142mm TA Dropouts

Bodo EV

http://www.bodoevs.com/bodoev/products_show.asp?product_id=13

Manufactured by Bodo Vehicle Group Limited, the Bodo EV is specially designed for strong power and extraordinary long service to facilitate super amazing rides. The Bodo Vehicle Company is a striking top in electric vehicles brand field in China and across the globe. Their Bodo EV will no doubt provide your riders with high-level riding satisfaction owing to its high-quality design, strength, breaking stability and speed.

Price: $799

Available countries

This item ships from China with buyers bearing the shipping costs and other variables prior to delivery.

Features

• Reliable
• Environment friendly
• Comfortable riding
• Fashionable
• Economical
• Durable – long service life
• Braking stability
• LED lighting technology

Specifications

• Maximum speed: 45km/h
• Range per charge: 50km per person
• Charging time: 8 hours
• Maximum Power: 3000W
• Motor type: Brushless DC Motor
• Load capacity: 100kg
• Battery type: Lead-acid battery
• Battery capacity: 60V 20AH
• Weight: w/o battery 47kg

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ThruwayBundle: Bundle for Building Real-time Apps in Symfony

ThruwayBundle

This a Symfony Bundle for Thruway, which is a php implementation of WAMP (Web Application Messaging Protocol).

Note: This project is still undergoing a lot of changes, so the API will change.

Quick Start with Composer

Install the Thruway Bundle

  $ composer require "voryx/thruway-bundle"

Update AppKernel.php (when using Symfony < 4)

$bundles = array(
    // ...
    new Voryx\ThruwayBundle\VoryxThruwayBundle(),
    // ...
);

Configuration

#app/config/config.yml

voryx_thruway:
    realm: 'realm1'
    url: 'ws://127.0.0.1:8081' #The url that the clients will use to connect to the router
    router:
        ip: '127.0.0.1'  # the ip that the router should start on
        port: '8080'  # public facing port.  If authentication is enabled, this port will be protected
        trusted_port: '8081' # Bypasses all authentication.  Use this for trusted clients.
#        authentication: false # true will load the AuthenticationManager
    locations:
        bundles: ["AppBundle"]
#        files:
#            - "Acme\\DemoBundle\\Controller\\DemoController"
#
# For symfony 4, this bundle will automatically scan for annotated worker files in the src/Controller folder
      

With Symfony 4 use a filename like: config/packages/voryx.yaml

If you are using the in-memory user provider, you'll need to add a thruway to the security firewall and set the in_memory_user_provider.

#app/config/security.yml

security: 
   firewalls:
        thruway:
            security: false	     

You can also tag services with thruway.resource and any annotation will get picked up

<service id="some.service" class="Acme\Bundle\SomeService">
    <tag name="thruway.resource"/>
</service>

Note: tagging a service as thruway.resource will make it public.

services:
    App\Worker\:
        resource: '../src/Worker'
        tags: ['thruway.resource']

Authentication with FOSUserBundle via WampCRA

Change the Password Encoder (tricky on existing sites) to master wamp challenge

#app/config/security.yml

security:
    ...
    encoders:
        FOS\UserBundle\Model\UserInterface:
            algorithm:            pbkdf2
            hash_algorithm:       sha256
            encode_as_base64:     true
            iterations:           1000
            key_length:           32

set voryx_thruway.user_provider to "fos_user.user_provider"

#app/config/config.yml

voryx_thruway:
    user_provider: 'fos_user.user_provider.username' #fos_user.user_provider.username_email login with email

The WAMP-CRA service is already configured, we just need to add a tag to it to have the bundle install it:

    wamp_cra_auth:
        class: Thruway\Authentication\WampCraAuthProvider
        parent: voryx.thruway.wamp.cra.auth.client
        tags:
            - { name: thruway.internal_client }

Custom Authorization Manager

You can set your own Authorization Manager in order to check if a user (identified by its authid) is allowed to publish | subscribe | call | register

Create your Authorization Manager service, extending RouterModuleClient and implementing RealmModuleInterface (see the Thruway doc for details)

// src/ACME/AppBundle/Security/MyAuthorizationManager.php


use Thruway\Event\MessageEvent;
use Thruway\Event\NewRealmEvent;
use Thruway\Module\RealmModuleInterface;
use Thruway\Module\RouterModuleClient;

class MyAuthorizationManager extends RouterModuleClient implements RealmModuleInterface
{
    /**
     * Listen for Router events.
     * Required to add the authorization module to the realm
     *
     * @return array
     */
    public static function getSubscribedEvents()
    {
        return [
            'new_realm' => ['handleNewRealm', 10]
        ];
    }

    /**
     * @param NewRealmEvent $newRealmEvent
     */
    public function handleNewRealm(NewRealmEvent $newRealmEvent)
    {
        $realm = $newRealmEvent->realm;

        if ($realm->getRealmName() === $this->getRealm()) {
            $realm->addModule($this);
        }
    }

    /**
     * @return array
     */
    public function getSubscribedRealmEvents()
    {
        return [
            'PublishMessageEvent'   => ['authorize', 100],
            'SubscribeMessageEvent' => ['authorize', 100],
            'RegisterMessageEvent'  => ['authorize', 100],
            'CallMessageEvent'      => ['authorize', 100],
        ];
    }

    /**
     * @param MessageEvent $msg
     * @return bool
     */
    public function authorize(MessageEvent $msg)
    {
        if ($msg->session->getAuthenticationDetails()->getAuthId() === 'username') {
            return true;
        }
        return false;
    }
}

Register your authorization manager service

     my_authorization_manager:
        class: ACME\AppBundle\Security\MyAuthorizationManager

Insert your service name in the voryx_thruway config

#app/config/config.yml

voryx_thruway:
    ...
        authorization: my_authorization_manager # insert the name of your custom authorizationManager
   ...

Restart the Thruway server; it will now check authorization upon publish | subscribe | call | register. Remember to catch error when you try to subscribe to a topic (or any other action) as it may now be denied and this will be returned as an error.

Usage

Register RPC

    use Voryx\ThruwayBundle\Annotation\Register;
    
    /**
     *
     * @Register("com.example.add")
     *
     */
    public function addAction($num1, $num2)
    {
        return $num1 + $num2;
    }

Call RPC

    public function call($value)
    {
        $client = $this->container->get('thruway.client');
        $client->call("com.myapp.add", [2, 3])->then(
            function ($res) {
                echo $res[0];
            }
        );
    }

Subscribe

     use Voryx\ThruwayBundle\Annotation\Subscribe;

    /**
     *
     * @Subscribe("com.example.subscribe")
     *
     */
    public function subscribe($value)
    {
        echo $value;
    }

Publish

    public function publish($value)
    {
        $client = $this->container->get('thruway.client');
        $client->publish("com.myapp.hello_pubsub", [$value]);
    }

It uses Symfony Serializer, so it can serialize and deserialize Entities

         use Voryx\ThruwayBundle\Annotation\Register;

    /**
     *
     * @Register("com.example.addrpc", serializerEnableMaxDepthChecks=true)
     *
     */
    public function addAction(Post $post)
    {
        //Do something to $post

        return $post;
    }

Start the Thruway Process

You can start the default Thruway workers (router and client workers), without any additional configuration.

$ nohup php app/console thruway:process start &

By default, the router starts on ws://127.0.0.1:8080

Workers

The Thruway bundle will start up a separate process for the router and each defined worker. If you haven't defined any workers, all of the annotated calls and subscriptions will be started within the default worker.

There are two main ways to break your application apart into multiple workers.

Use the worker property on the Register and Subscribe annotations. The following RPC will be added to the posts worker.

  use Voryx\ThruwayBundle\Annotation\Register;

  /**
  * @Register("com.example.addrpc", serializerEnableMaxDepthChecks=true, worker="posts")
  */
  public function addAction(Post $post)

Use the @Worker annotation on the class. The following annotation will create a worker called chat that can have a max of 5 instances.

  use Voryx\ThruwayBundle\Annotation\Worker;

  /**
  * @Worker("chat", maxProcesses="5")
  */
  class ChatController

If a worker is shut down with anything other than SIGTERM, it will automatically be restarted.

More Commands

To see a list of running processes (workers)

$ php app/console thruway:process status

Stop a process, i.e. default

$ php app/console thruway:process stop default

Start a process, i.e. default

$ php app/console thruway:process start default

Javascript Client

For the client, you can use AutobahnJS or any other WAMPv2 compatible client.

Here are some examples

Symfony 4 Quick Start

composer create-project symfony/skeleton my_project

cd my_project

composer require symfony/expression-language

composer require symfony/annotations-pack

composer require voryx/thruway-bundle:dev-master

Create config/packages/my_project.yml with the following config:

voryx_thruway:
    realm: 'realm1'
    url: 'ws://127.0.0.1:8081' #The url that the clients will use to connect to the router
    router:
        ip: '127.0.0.1'  # the ip that the router should start on
        port: '8080'  # public facing port.  If authentication is enabled, this port will be protected
        trusted_port: '8081' # Bypasses all authentication.  Use this for trusted clients.

Create the controller src/Controller/TestController.php

<?php
namespace App\Controller;

use Voryx\ThruwayBundle\Annotation\Register;

class TestController
{
    /**
     * @Register("com.example.add")
     */
    public function addAction($num1, $num2)
    {
        return $num1 + $num2;
    }
}

Test to see if the RPC has been configured correctly bin/console thruway:debug

 URI             Type Worker  File                                                  Method    
 com.example.add RPC  default /my_project/src/Controller/TestController.php         addAction 

For more debug info for the RPC we created: bin/console thruway:debug com.example.add

Start everything: bin/console thruway:process start

The RPC com.example.add is now available to any WAMP client connected to ws://127.0.0.1:8081 on realm1.

Author: Voryx
Source Code: https://github.com/voryx/ThruwayBundle 
License: 

#php #symfony