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Data Fabric, Data Mesh, Knowledge Graph, Data Management, Knowledge Management.
few years, we’ve heard about data fabric and data mesh. Customers often ask, “Do I need a data fabric or a data mesh?” But this is the wrong question as these are not competing but complementing paradigms with a symbiotic relationship to localize data ownership at the domain level and create
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reusable data products across the organization. While data mesh takes a bottom-up approach, data fabric is top-down. Coalescence of data fabric and data mesh, powered by semantic knowledge graphs, can lead to significant reduction of ETL, data moving, and data copying and eliminating redundancies.
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Data Fabric Most organizations struggle to stitch data from disparate data sources in a coherent and useful manner. Data Fabric aims to make it easy to link, consolidate, and meaningfully describe data. It does that by combining several data management techniques like semantic data integration,
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data orchestration, semantically driven data pipelines, semantic data catalogs, and automation. By providing a consolidated user experience and access to data, data fabric helps organizations manage their data, regardless of the form or the location where it’s stored. It removes friction and
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mitigates cost as there’s no need to make copies of the data or pay for its storage or movement. Data fabric doesn’t involve any centralization into a data lake or a data warehouse. It requires sourcing the data from its current location by implementing service-level agreements (SLAs) from each of
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the business units. Thus, it delegates the responsibilities for datasets closer to where data is produced and utilizes ML/AI to create a semantic approach to accessing it. By bringing data and metadata together, data fabric powered by KGs translates disparate data systems into useful organizational
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knowledge. As data changes, the metadata gets updated dynamically simplifying data ingestion, access, and storage. Data Mesh Unlike data lakehouses and cloud data warehouses, data mesh doesn’t differentiate between analytical and transactional systems. It marries organizational patterns with
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technology and architectural approaches. By promoting data autonomy, it enables users to make domain-related decisions. They no longer have to rely on centralized data engineering or IT teams to provide data access, a common obstacle in most organizations. Instead, data mesh distributes data
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ownership and responsibilities, reduces dependencies across services, and thereby delivers more value at velocity. The following diagram provides a high-level picture of the data mesh. The centralized services in a data mesh approach, powers data sharing, reusability, interoperability and relieves
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domain teams from performing repeated data ingestion, processing and storage steps. Each domain builds data products with localized data catalog and domain-specific business information. Data products are the core concepts of data mesh as it encapsulates code, data, infrastructure, and metadata and
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is created and offered to consumers as self-service. Data mesh requires data contracts between producers and consumers, ensuring SLAs as data flows within the organization. However, this requires a culture shift from how data teams work. Knowledge graphs ensure that data contracts are standardized,
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semantically correct, and aligned. Integration of knowledge graphs with data mesh leads to emergence of a “semantic data mesh”. It provides data with context and meaning across different business units within an organization. This promotes data discoverability, interoperability, augmentation,
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enrichment and explainability with AI and ML. Major Takeaways Data management is not about just managing data. It is about managing the knowledge that is inherent within the different business units and departments of an organization. It’s about managing data with a context to bring actionable
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insights. For most organizations, context and semantics are the missing piece of the puzzle. Data fabric and data mesh offer a new way of looking at data management for enterprises. They are evolutionary architectures, which are still a work in progress. Knowledge graphs can help unite data fabric
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and data mesh. Without knowledge graphs and semantics both of these architectures will fail. By empowering semantics to connect data between users, systems, and applications consistently, unambiguously, and confidently, knowledge graphs ensure data quality, helping organizations cross the chasm
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from information to wisdom. By empowering semantics to connect data between users, systems, and applications consistently, unambiguously, and confidently, knowledge graphs ensure data quality, helping organizations cross the chasm from information to wisdom. Sumit Pal, Strategic Technology Director
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Introduction to PCA Principal Component Analysis (PCA) is a statistical technique that simplifies the complexity in high-dimensional data while retaining trends and patterns. It does so by transforming the data into fewer dimensions, which act as summaries of features, called principal components
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(PCs). These components are orthogonal to each other, ensuring that they represent independent variances in the data. Dataset Overview In our case study, we’re using an Airbnb listings dataset that contains various features like location, room type, price, and more. Our aim is to uncover underlying
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patterns in this dataset which can help us segment the listings into meaningful groups. import pandas as pd from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.decomposition import PCA from sklearn.cluster import KMeans # Load the dataframe from the CSV file df =
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pd.read_csv('https://raw.githubusercontent.com/fenago/datasets/main/airbnb.csv') Data Preprocessing Steps Before diving into PCA, we need to ensure that our data is clean and in the right format for analysis: Missing Values: We handled missing values by filling them with the mean of their
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respective columns, ensuring no data point was left behind. Categorical Encoding: We converted categorical variables such as host_is_superhost, neighbourhood, property_type, and instant_bookable into numeric using label encoding, while the city feature was one-hot encoded. This step is crucial as
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PCA requires numeric input. Feature Scaling: We used StandardScaler to scale our features. Scaling is vital for PCA because it is sensitive to the variances of the initial variables. # Fill missing values with the mean of the column df_filled = df.fillna(df.mean()) # Convert categorical columns to
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numeric using label encoding # Initialize label encoder label_encoder = LabelEncoder() # Columns to label encode label_encode_columns = ['host_is_superhost', 'neighbourhood', 'property_type', 'instant_bookable'] # Apply label encoding to each column for column in label_encode_columns:
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df_filled[column] = label_encoder.fit_transform(df_filled[column]) # Apply one-hot encoding to 'city' using get_dummies df_filled = pd.get_dummies(df_filled, columns=['city']) # Redefine and refit the scaler to the current dataset scaler = StandardScaler() scaled_features =
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scaler.fit_transform(df_filled) PCA Application Applying PCA to our scaled dataset, we decided on three principal components. This number is often chosen based on the explained variance, which represents how much information each component captures from the data. # Apply PCA pca =
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PCA(n_components=3) pca_result = pca.fit_transform(scaled_features) KMeans Clustering With our data now in three-dimensional PCA space, we applied KMeans clustering to identify four distinct clusters. This approach groups data points so that those within each cluster are more similar to each other
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than to those in other clusters. # Apply KMeans clustering on the PCA result kmeans_pca = KMeans(n_clusters=4, random_state=42) kmeans_pca.fit(pca_result) Analysis of PCA Components Each principal component represents a combination of original features, but what exactly are they capturing? # Get
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the PCA components (loadings) pca_components = pca.components_ Let’s delve into the loadings of each PCA: PC1: It seems to heavily weight the geographical coordinates (latitude and longitude), suggesting that this component may represent the geographical distribution of listings. PC2: This
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component is inversely related to the host_since_datekey, indicating it might be capturing some aspect of the host's experience or tenure. PC3: With high loadings for accommodates and listing_size_sqft, this component may reflect the size and capacity of the listing. Inverse Transformation By
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inverse transforming the PCA cluster centers, we map our clusters back to the original space to interpret the centroids in terms of the original features. This step is like translating our PCA results back into a language we can understand. # Inverse transform the cluster centers from PCA space
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back to the original feature space original_space_centroids = scaler.inverse_transform(pca.inverse_transform(kmeans_pca.cluster_centers_)) # Create a new DataFrame for the inverse transformed cluster centers with column names centroids_df = pd.DataFrame(original_space_centroids,
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columns=df_filled.columns) # Calculate the mean of the original data for comparison original_means = df_filled.mean(axis=0) # Prepare the PCA loadings DataFrame pca_loadings_df = pd.DataFrame(pca_components, columns=df_filled.columns, index=[f'PC{i+1}' for i in range(3)]) Centroid Analysis The
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centroids of our clusters, when compared to the mean of the original data, tell us about the central tendency of each cluster. For instance, if a centroid has a higher price value than the mean, the corresponding cluster might represent more premium listings. # Append the mean of the original data
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to the centroids for comparison centroids_comparison_df = centroids_df.append(original_means, ignore_index=True) # Store the PCA loadings and centroids comparison DataFrame for further analysis pca_loadings_df.to_csv('/mnt/data/pca_loadings.csv', index=True)
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centroids_comparison_df.to_csv('/mnt/data/centroids_comparison.csv', index=False) pca_loadings_df, centroids_comparison_df.head() # Displaying the PCA loadings and the first few rows of the centroids comparison DataFrame Conclusion PCA has allowed us to reduce the dimensionality of our dataset,
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revealing intrinsic patterns that weren’t initially apparent. When combined with clustering, we can segment our listings into distinct groups, each representing a different facet of the Airbnb market. Deeper Dive Step 1: Determine the Optimal Number of PCA Components When we perform PCA, we
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transform the original set of features into a new set of orthogonal features called principal components (PCs). Each principal component captures a certain percentage of the total variance in the dataset. The first principal component captures the most variance, and each subsequent component
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captures less. By looking at the cumulative explained variance, we can see how much of the total variance is captured as we include more and more components. The cumulative explained variance plot shows the proportion of the dataset’s total variance that is captured by including up to �n principal
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components. The idea is to choose the smallest number of principal components that still capture a large proportion of the total variance. A common rule of thumb is to choose enough components to capture at least 95% of the total variance, which allows us to reduce dimensionality while retaining
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most of the information in the dataset. Let’s revisit the cumulative explained variance plot to determine the number of components that meet this criterion. We’ll look for the point where the cumulative explained variance exceeds 95%, which is commonly considered sufficient to capture most of the
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information in the dataset. This number of components is often a good balance between information retention and dimensionality reduction. We’ll analyze the plot again and provide a more intuitive explanation. # Fit PCA to the data without reducing dimensions and compute the explained variance ratio
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pca_full = PCA() pca_full.fit(scaled_features) # Calculate the cumulative explained variance ratio explained_variance_ratio = pca_full.explained_variance_ratio_ cumulative_explained_variance = explained_variance_ratio.cumsum() # Plot the cumulative explained variance ratio to find the optimal
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number of components plt.figure(figsize=(10, 6)) plt.plot(range(1, len(cumulative_explained_variance) + 1), cumulative_explained_variance, marker='o', linestyle='--') plt.title('Cumulative Explained Variance by PCA Components') plt.xlabel('Number of PCA Components') plt.ylabel('Cumulative Explained
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Variance') plt.grid(True) plt.axhline(y=0.95, color='r', linestyle='-') # 95% variance line for reference plt.text(0.5, 0.85, '95% cut-off threshold', color = 'red', fontsize=16) # Determine the number of components that explain at least 95% of the variance optimal_num_components =
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len(cumulative_explained_variance[cumulative_explained_variance >= 0.95]) + 1 # Highlight the optimal number of components on the plot plt.axvline(x=optimal_num_components, color='g', linestyle='--') plt.text(optimal_num_components + 1, 0.6, f'Optimal Components: {optimal_num_components}', color =
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'green', fontsize=14) plt.show() # Returning the optimal number of components optimal_num_components The updated plot provides a clearer picture of how the cumulative explained variance increases with the number of principal components. The green vertical line marks the point where the number of
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components collectively explains at least 95% of the total variance in the dataset. From the plot, we can see that this threshold is crossed with 9 principal components. This means that by using 9 components, we can capture 95% of the variability in the data, which is often considered sufficient
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for many applications. This is a significant reduction from the original number of features while still retaining most of the information. So, in the context of our analysis, instead of using all the original features, we could perform PCA and reduce the dimensionality to 9 principal components to
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achieve a simpler, yet still informative, representation of our dataset. Step 2: Redo PCA with 9 Components # Redo PCA with 9 components pca_9 = PCA(n_components=9) pca_result_9 = pca_9.fit_transform(scaled_features) # Get the PCA loadings for 9 components pca_components_9 = pca_9.components_ #
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Analyze the PCA loadings to determine which features contribute most to each of the 9 principal components # We will look at the absolute values of the loadings to assess their contribution pca_loadings_analysis_9 = pd.DataFrame( pca_components_9, columns=df_filled.columns, index=[f'PC{i+1}' for i
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in range(9)] ).abs().T # Transpose to have features as rows # Sorting the loadings for each component to see the top contributing features top_features_per_pc_9 = pca_loadings_analysis_9.apply(lambda s: s.abs().nlargest(5).index.tolist(), axis=0) top_features_per_pc_9 To provide the actual loading
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scores for each feature in the first nine principal components, we can output the numerical values of the PCA loadings matrix. The loading for each feature in a given principal component will be displayed, showing their contributions. # Display the actual loadings for the top 5 features for each of
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the 9 principal components # We will extract the top 5 features based on the absolute values of the loadings and display their actual loadings import numpy as np # Function to get top n features for each principal component with their loadings def get_top_features_loadings(pca_loadings,
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n_features): top_features = {} for i in range(pca_loadings.shape[0]): # Get the index of the n largest absolute values in the i-th principal component top_indices = np.argsort(np.abs(pca_loadings[i]))[-n_features:] # Create a dictionary of the top features and their loadings for the i-th principal
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component top_features[f'PC{i+1}'] = {df_filled.columns[j]: pca_loadings[i][j] for j in top_indices} return top_features # Get the top 5 features and their loadings for each of the 9 principal components top_features_loadings_9 = get_top_features_loadings(pca_components_9, 5)
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top_features_loadings_9_df = pd.DataFrame(top_features_loadings_9).T top_features_loadings_9_df The table above displays the actual loadings for the top features of each of the first nine principal components. The loadings are the coefficients that represent how much each feature contributes to the
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principal component. Here’s a summary of the top contributing features to each principal component along with their loadings: PC1: Geographical features and cities have the strongest influence, with loadings showing positive and negative values, indicating opposite directions on the map. PC2:
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Host-related features like host_since_datekey and host_id have high negative loadings, meaning these features have a strong inverse relationship with PC2. PC3: Property-related features such as accommodates, listing_size_sqft, and bedrooms have strong positive loadings, meaning they directly
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influence PC3. PC4 to PC9: Various other features related to the city, property type, booking options, and review scores contribute to these components with varying degrees of positive and negative loadings. To interpret these loadings: A positive loading means that as the feature value increases,
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the score on the principal component also increases. A negative loading means that as the feature value increases, the score on the principal component decreases. The magnitude of the loading (how far it is from zero) indicates the strength of the relationship between the feature and the principal
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component. To perform a detailed analysis and infer what each PCA seems to mean, one would need to consider the domain knowledge of the dataset and understand how each of the top features relates to the context of Airbnb listings. This involves considering what each feature represents (e.g.,
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location, property size, host experience) and how they might group together to form a theme represented by the principal component. We’ve successfully performed PCA with 9 principal components and have listed the top five features that contribute most to each component. Here’s how we interpret the
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loadings to determine feature contribution: Interpreting PCA Loadings and Feature Contributions The loadings of a PCA component reflect the correlation between the original variables and the principal component. Here’s how to interpret these loadings: High Positive Loading (close to 1): Indicates
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that the feature has a strong positive association with the component. High Negative Loading (close to -1): Indicates that the feature has a strong negative association with the component. Loading Close to 0: Indicates that the feature has a weak association with the component. The top contributing
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features for each principal component are those with the highest absolute loadings, regardless of whether they are positive or negative. These features are considered to have the most impact on the component’s variance. Analysis and Interpretation of the 9 PCAs Now, we’ll interpret the themes of
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each of the 9 principal components based on the top contributing features: PC1: Dominated by city-related features and geographical coordinates, suggesting a theme of geographical location. PC2: Influenced by host identifiers and dates, indicating a theme of host experience or tenure. PC3: Includes
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features related to the size and capacity of the listing, pointing to a theme of property size and accommodation capacity. PC4: Features city-related variables and acceptance rate, hinting at a theme of hosting preferences and location desirability. PC5: Marked by city and price, which may reflect
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the theme of pricing strategy in different locations. PC6: Contains instant bookable and host superhost status, suggesting a theme of hosting services and amenities. PC7: Features response rate and review scores, pointing to a theme of host responsiveness and guest satisfaction. PC8: Also includes
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host total listings and review scores, indicating a theme of host portfolio and quality of experience. PC9: Captures neighborhood and host listing counts, which might represent neighborhood popularity and host activity. How to Conduct Thematic Analysis To conduct a thematic analysis of PCA
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components: Sort PCA Loadings: Sort the features by their loadings for each principal component. Identify Top Features: Identify the top features with the highest absolute loadings. Understand Feature Significance: Understand the significance of these features in the context of your dataset. Look
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for Patterns: Look for patterns among the top features to deduce a theme. Consider Positive and Negative Contributions: Note that features with high positive loadings and those with high negative loadings contribute differently to the theme. Validate Themes: Validate your inferred themes with
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We’ll Create a Geogebra program to help us with our linear programming — #PySeries#Episode 02 First, we’ve got this problem that comes from the math to your course guide from the Pingree school. It is the tape’s verse CDs example: Suppose you want to buy some tapes and CDs. Does anyone really buy
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tapes anymore? Anyway, you can buy up to 11 tapes; You can buy up to 7 CDS, but you want at least 3; You must get enough tapes or CDs to hold at least 10 hours of music; Each tape holds about 45 minutes of music and each CD holds an hour a tape costs $8 and a CD costs $12; So how many tapes and CDs
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should you buy to minimize your cost? Let’s see the solution using Geogebra than in the end I will present the Python solution. 1# Define Your Variables: x=The number of tapes to buy; y=The number of CDs to buy; 2# Translate your constraints: x<=11 3<=y>=7 600<=45x+60y 3# Type your objective
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Function: T(x,y)=8x+12y 4# Open a Geogebra New Activity session, Hit Geogebra, Create an Applet and get a name to your applet (mine is GeoPlusLinearProg): Fig 1. Opening and Creating New Activity in Geogebra App. You can expand the applet according to your screen resolution: Fig 2. Expanding the
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Applet view! Just fine! 5# On Geogebra, on the left, type the inequality as well as equality like this: a: x <= 11 b: x = 11 c: 3 <= y <= 7 d: y = 3 e: y = 7 f: 45*x + 6*y >= 600 g: 3*x + 4*y = 40 The regions in the graph are presented, now configure them according to your taste, clicking on each
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inequality and changing the style and color properties. Here are my settings. Fig 3. Entering all the constraints and equations. 6#Now, click Point > Intercept and create 4 points, in the region of interest: Fig 4. Separating the solution region. 7# On Geogebra, open a Spreadsheet and type
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(hamburger icon>View>Spreedsheet located at top-right): Fig 5. Delimiting each intercept points of the solution. Now, in the spreadsheet type this (drag the little rectangle down): Points - Total Cost, T A =8*x(A2)+12*y(A2) B =8*x(A3)+12*y(A3) C =8*x(A4)+12*y(A4) D =8*x(A5)+12*y(A5) Fig 6. We found
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the region of the solution; Any of the combinations are valid; the lowest cost is 9.33 Tape and 3 CDs (A3)=$110.67. Any of the vertices of the trapeze serve as an answer. I choose the second option(9 Tapes and 3 CDs). Let’s see what python decides … 8# Python Solution: from pulp import *
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prob=LpProblem('Example', LpMaximize) x1=LpVariable("Tape", 0) x2=LpVariable("CD", 0) prob += x1<=11 prob += x2>=3 prob += x2<=7 prob += 45*x1+60*x2<=600 prob += 8*x1+12*x2 prob.solve() for v in prob.variables(): print(v.name,"=", v.varValue) print("Total Cost = ", value(prob.objective)) CD = 7.0
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Tape = 4.0 Total Cost = 116.0 Fig 7. Using Visual Studio code App; please see this post about it: Python 4 Engineers — Exercises! linked bellow… # Episode 01 #PySeries # An overview of the Opportunities Offered by Python in Engineering And that’s all for now! I hope you were impressed with the
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Geogebra app like I am! Bye for now! See you in the next episode of Python Series! https://docs.google.com/document/d/1DkycafWLUZeprvnTQcSgaasyniTWTeDe4cEzEnN6hes/edit?usp=sharing References & Credits Fig 8. Here is the inspiration for this lesson! Thanks to Wicked Math 👌 Posts Related:
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Python, Geogebra, Linear Programming, Linear Regression Python, Python3.
00Episode#PySeries — Python — Jupiter Notebook Quick Start with VSCode — How to Set your Win10 Environment to use Jupiter Notebook 01Episode#PySeries — Python — Python 4 Engineers — Exercises! An overview of the Opportunities Offered by Python in Engineering! 02Episode#PySeries — Python — - We’ll
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medium
| 8,509 |
Python, Geogebra, Linear Programming, Linear Regression Python, Python3.
Create a Geogebra program to help us with our linear programming (this one) 03Episode#PySeries — Python — Python 4 Engineers — More Exercises! — Another Round to Make Sure that Python is Really Amazing! 04Episode#PySeries — Python — Linear Regressions — The Basics — How to Understand Linear
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medium
| 8,510 |
Python, Geogebra, Linear Programming, Linear Regression Python, Python3.
Regression Once and For All! 05Episode#PySeries — Python — NumPy Init & Python Review — A Crash Python Review & Initialization at NumPy lib. 06Episode#PySeries — Python — NumPy Arrays & Jupyter Notebook — Arithmetic Operations, Indexing & Slicing, and Conditional Selection w/ np arrays.
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medium
| 8,511 |
Python, Geogebra, Linear Programming, Linear Regression Python, Python3.
07Episode#PySeries — Python — Pandas — Intro & Series — What it is? How to use it? 08Episode#PySeries — Python — Pandas DataFrames — The primary Pandas data structure! It is a dict-like container for Series objects 09Episode#PySeries — Python — Python 4 Engineers — Even More Exercises! — More
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medium
| 8,512 |
Python, Geogebra, Linear Programming, Linear Regression Python, Python3.
Practicing Coding Questions in Python! 10Episode#PySeries — Python — Pandas — Hierarchical Index & Cross-section — Open your Colab notebook and here are the follow-up exercises! 11Episode#PySeries — Python — Pandas — Missing Data — Let’s Continue the Python Exercises — Filling & Dropping Missing
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medium
| 8,513 |
Python, Geogebra, Linear Programming, Linear Regression Python, Python3.
Data 12Episode#PySeries — Python — Pandas — Group By — Grouping large amounts of data and compute operations on these groups 13Episode#PySeries — Python — Pandas — Merging, Joining & Concatenations — Facilities For Easily Combining Together Series or DataFrame 14Episode#PySeries — Python — Pandas —
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medium
| 8,514 |
Python, Geogebra, Linear Programming, Linear Regression Python, Python3.
Pandas Dataframe Examples: Column Operations 15Episode#PySeries — Python — Python 4 Engineers — Keeping It In The Short-Term Memory — Test Yourself! Coding in Python, Again! 16Episode#PySeries — NumPy — NumPy Review, Again;) — Python Review Free Exercises 17Episode#PySeries — Generators in Python —
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medium
| 8,515 |
Python, Geogebra, Linear Programming, Linear Regression Python, Python3.
Python Review Free Hints 18Episode#PySeries — Pandas Review…Again;) — Python Review Free Exercise 19Episode#PySeries — MatlibPlot & Seaborn Python Libs — Reviewing theses Plotting & Statistics Packs 20Episode#PySeries — Seaborn Python Review — Reviewing theses Plotting & Statistics Packs
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medium
| 8,516 |
Kotlin, Kotest, Property Based Testing.
Foto: https://janmidtgaard.dk/quickcheck/ In the last article I showed how mutation testing can be used to check the tests, that are written for an application, for quality regarding detection of changes in productive code. Mutation testing is a testing type, which does not run against the
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medium
| 8,517 |
Kotlin, Kotest, Property Based Testing.
productive code to check for the correct behavior, but changing the productive code to check if tests recognize the change. Today I want to introduce another testing type, which can help to increase the quality of the tests, but this time directly inside the existing tests by adding randomness —
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medium
| 8,518 |
Kotlin, Kotest, Property Based Testing.
Property-based testing. Introduction What is Property-based testing? Property-based tests are designed to test the aspects of a property that should always be true. They allow for a range of inputs to be programmed and tested within a single test, rather than having to write a different test for
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medium
| 8,519 |
Kotlin, Kotest, Property Based Testing.
every value that you want to test. Property-based testing is a form of fuzzing (Fuzz testing). The main goal is to provide random input data to tests, so that all possible boundaries are covered. Instead of testing only for by developer specified input data, all as valid defined input data is used.
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medium
| 8,520 |
Kotlin, Kotest, Property Based Testing.
This is a very theoretical description, to better understand this, let’s look on an example. The focus is on showing how Property-based testing is working, not showing a real world example: val minDate: LocalDate = LocalDate.of(2022, 1, 1) val maxDate: LocalDate = LocalDate.of(2022, 12, 31) class
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medium
| 8,521 |
Kotlin, Kotest, Property Based Testing.
ApplicationService { fun createOutput(inputDto: InputDto): OutputDto { return mapInputToOutput(inputDto) } private fun mapInputToOutput(inputDto: InputDto): OutputDto { return OutputDto( date = inputDto.date, amount = inputDto.amount, positions = mapInputPositionToOutputPosition( positions =
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medium
| 8,522 |
Kotlin, Kotest, Property Based Testing.
inputDto.positions ) ) } private fun mapInputPositionToOutputPosition(positions: List<InputPositionDto>): List<OutputPositionDto> { require(positions.isNotEmpty()) { "Positions must not be empty." } val sum = positions.fold(0.0) { acc, next -> acc + next.value } require(sum.compareTo(0.0) > -1) {
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medium
| 8,523 |
Kotlin, Kotest, Property Based Testing.
"Sum of positions amount must be greater than 0.0 but is $sum" } return positions.map { OutputPositionDto( name = it.name, value = it.value.toEURCurrency() ) } } } data class InputDto( val date: LocalDate, val amount: Int, val positions: List<InputPositionDto> ) data class InputPositionDto( val
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medium
| 8,524 |
Kotlin, Kotest, Property Based Testing.
name: String, val value: Double ) data class OutputDto( val date: LocalDate, val amount: Int, val positions: List<OutputPositionDto> ) { init { require(date in minDate..maxDate) { "Date '${date}' must be within '$minDate' and '${maxDate}'." } require(amount >= 0) { "Amount '$amount' must be greater
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medium
| 8,525 |
Kotlin, Kotest, Property Based Testing.
or equal null." } } } data class OutputPositionDto( val name: String, val value: MonetaryAmount ) fun Double.toEURCurrency(): MonetaryAmount { return BigDecimal(this, MathContext(2, RoundingMode.HALF_UP)).ofCurrency<FastMoney>("EUR".asCurrency(), typedMonetaryContext<FastMoney> { setPrecision(2) })
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medium
| 8,526 |
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