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| LASSO (Least Absolute Shrinkage and Selection Operator) |
Introduction |
| Skills Network Labs (SN Labs, IBM) |
Introduction |
| Leveraging precision, speed, and automation: Integrating Mask R-CNN and YOLOv8 |
Introduction |
| "Extract, Transform, Load" (ETL) and " Extract, Load, Transform" (ELT) processes |
Introduction |
| YOLOv8 (You Only Look Once, version 8) |
Introduction |
| Comparison btween data lake and data warehouse |
Introduction |
| Using proxy labels, building a labeling system, and utilizing a labeling service when historical labeled data is unavailable for ML projects |
Introduction |
| OLS (Ordinary Least Squares) regression model |
Introduction |
| Big data lifecycle |
Introduction |
| Impact of corpus narrowness on language model training |
Introduction |
| Guess and check algorithm with a combination of a for loop and an if statement |
Introduction |
| Labor cost of data analysis with and without automation and ML techniques |
Introduction |
| Trade-off between minimizing loss and minimizing complexity |
Introduction |
| Mean squared error (MSE) (L2 loss function, Euclidean loss) |
Introduction |
| Python libraries for Bayesian ML techniques |
Introduction |
| L1 Loss (Absolute Loss or Mean Absolute Error (MAE)) |
Introduction |
| Language Model |
Introduction |
| Replaces a symbol/character/letter in a string |
Introduction |
| Plot with letters/words/character as x-/y-axis |
Introduction |
| Linear Programming (LP) algorithm |
Introduction |
| Constraint ("limit"/"range") satisfaction |
Introduction |
| Local Search in ML |
Introduction |
| Likelihood weighting |
Introduction |
| First-order logic (FOL) |
Introduction |
| Logical statements |
Introduction |
| Modus ponens (a logical inference rule) |
Introduction |
| Logic Puzzle in ML |
Introduction |
| Propositional Logic Algorithms in ML |
Introduction |
| Cost/loss function versus reward function |
Introduction |
| Linearization |
Introduction |
| Linear Quadratic Regulation (LQR) |
Introduction |
| Q-Learning with Function Approximation (Deep Q-Network - DQN) |
Introduction |
| Nonlinear extensions of Independent Component Analysis (ICA) |
Introduction |
| Maximum Likelihood Estimation (MLE) of single Gaussian (normal) distribution |
Introduction |
| Learning algorithm (ensemble learning) and pipeline |
Introduction |
| Logistic regression as a one-neuron/single-layer neural network (connection between linear & activation parts) |
Introduction |
| Number of neurons and layers in neural network |
Introduction |
| Comparison among sigmoid, hyperbolic tangent (tanh) and rectified linear unit (ReLU) functions |
Introduction |
| Neuron (= linear + activation) introduction |
Introduction |
| Gini Loss |
Introduction |
| Misclassification loss in decision trees |
Introduction |
| Linear model versus polynomial model |
Introduction |
| No Free Lunch Theorems |
Introduction |
| Learning theory |
Introduction |
| Leave-One-Out Cross-Validation (LOOCV) |
Introduction |
| "Norm" of parameters, and L1 Norm (Manhattan Norm) and L2 Norm (Euclidean Norm) |
Introduction |
| Logistic regression and Naive Bayes |
Introduction |
| Open datasets, and open-source tools and libraries for ML practice |
Introduction |
| |
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| Learning theory |
Introduction |
| |
✔ |
Generalization |
Introduction |
| |
✔ |
Bias and variance, and bias-variance trade-off in ML |
Introduction |
| |
✔ |
Model Complexity |
Introduction |
| |
✔ |
Convergence and Optimization |
Introduction |
| |
✔ |
Sample Complexity |
Introduction |
| |
✔ |
Probably Approximately Correct (PAC) learning |
Introduction |
| |
✔ |
Margin Theory |
Introduction |
| |
✔ |
No Free Lunch Theorems |
Introduction |
| |
|
| Layers |
Introduction |
| ◆ |
Categorical features preprocessing layers |
Introduction |
| ◆ |
Keras preprocessing layers |
Introduction |
| ◆ |
tf.keras.layers.Hashing |
Introduction |
| ◆ |
tf.keras.layers.CategoryEncoding |
Introduction |
| ◆ |
tf.keras.layers.Discretization |
Introduction |
| ◆ |
keras.layers.normalization |
Introduction |
| ◆ |
tf.keras.layers.StringLookup |
Introduction |
| ◆ |
tf.keras.layers.IntegerLookup |
Introduction |
| ◆ |
tf.keras.layers.TextVectorization |
Introduction |
| |
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| Output the web links obtained by Google Search |
Introduction |
| Mirror/reflect image from left to right/from top to bottom |
Introduction |
| Non-linearity in machine learning |
Introduction |
| (Find) file size/find the largest/smallest file in a directory/folder |
Introduction |
| .pack(side=LEFT)/.pack(side=RIGHT)/.place(x=, y=) --- position of the buttons |
(Code) |
| Locate/find the center of a bright (maximum/highest intensity) spot in an image & find nearest white pixel to a given/specifical pixel location on an binary image |
Introduction |
| Linear algebra |
Introduction |
| .LabelEncoder() |
Introduction |
| Rectified Linear Units (ReLUs) |
Introduction |
| Least squares fit |
Introduction |
| Long short-term memory (LSTM) |
Introduction |
| Script execution limited by retry time |
Introduction |
| Line detection on image |
Introduction |
| Automatically review, scroll, click webpage and its link |
Introduction |
| Mouse left single click |
Introduction |
| Left click |
(Introduction) |
| Left click a specific position |
Introduction |
| Lock desktop |
Introduction |
| Launch the existing opened application if there is or start a new one if there is not |
Introduction |
| Loops (e.g. for loop) for 2D (two-dimensional) plot |
Introduction |
| Convert capital alphabet letters/characters to number |
Introduction |
| Type capital letters |
Introduction |
| Modify/replace the line in a text file if a line contains specific string |
Introduction |
| Move the mouse/cursor to the left or right |
Introduction |
| Bind/link multiple commands to buttons |
Introduction |
| Copy and then store it into memory and it can be pasted for use later |
Introduction |
| Get the latest/newest/most recent file in a folder within certain time/days |
Introduction |
| Select/input a folder/directory/path for later to be called to use |
Introduction |
| Close file after reading a file: avoid file locking |
Introduction |
| watchdog to look for filesystem changes |
Introduction |
| Check the letters and symbols starting and ending with |
code. |
| Calibrate and put a scale bar, and draw a line segment on an image |
Introduction |
| Calculator of length accuracy in 3D structure |
Introduction |
| Merge/combine two text files into a new text file, add a new line to the beginning of a text file |
Introduction |
| Count the numbers of uppercase letters, lowercase letters and spaces in a string and then swap the cases of the letters. |
code |
| Draw a line in a image |
code |
| Draw lines manually and then label them with arrows |
code |
| Remove letters or characters on either side (both left and right sides) and stops when neither such letters no characters on either side |
code |
| Calculate the coordinates of a point in a given rectangle and the distance of a given point to a line |
code |
| Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values |
CSV: Introduction. code. |
| Bail out/terminate of a loop |
code |
| Loop through a string from left to right |
code1, code2 |
| Loop through numbers in a range |
code |
| Load/launch images and ColorMixing in DigitalMicrograph |
Introduction |
| Draw lines and arrows in a ppt |
Introduction |
Linear correlation between two variables with Pearson Correlation Coefficient, Spearman Rank Correlation Coefficient, Kendall's Tau, Linear Regression, Coefficient of Determination and Correlation Ratio
|
Introduction |
| .LINE |
(code) |
| Stability/reliability of locateCenterOnScreen() |
Introduction |
| Find latitude and longitude of a place in a map |
Introduction |
| Print and set file path as a variable (e.g. convert all characters in the pathname to lowercase) |
Introduction |
| Check if a directory is empty; find the position/index of a particular file/folder in a file directory/path; remove folder or file level by level (or layer by laer) from its directory/path |
Introduction |
| matplotlib.pyplot axis/text color (title, xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) |
Introduction |
| top and left for pptx |
Introduction |
| break and functions to exit a loop: Stop the loop, e.g. while loop immediately. code1, code2 |
Introduction. |
| check if a letter is in a string |
Introduction |
| Plot pixel intensities (histogram) along a line of an image |
Introduction |
| Global access to a local variable inside a function from outside of the function externally |
Introduction |
| Top (ranking, best, must know) Python libraries/modules |
Introduction |
| Measure length/distance on an image w/o calibrated bar |
Introduction |
| Rotate (alignment) an image by line along the x- or y-axis |
Introduction |
| Get pixel location/coordinates on an image using mouse click/events |
Introduction |
| Crop/snip part of a image with definition by a pixel line |
Introduction |
| Quit/exit/stop a process (including by pressing a letter) |
Introduction |
| Supervised learning |
Introduction |
| Extract the index of a string element in a list |
Introduction |
| Unsupervised learning |
Introduction |
| Reinforcement learning |
Introduction |
| Break/exit/skip a function/code line after a certain time |
Introduction |
| Libraries used to convert incident documents into numerical vectors |
Introduction |
| Linear Support Vector Classifier (Linear SVC) |
Introduction |
| Change/capitalize the case of the first letter of a string (first letter: uppercase; other letters: lowercase) |
Introduction |
| Ranking/most popular programming languages for data analysts |
Introduction |
| Remove \n in string or new line in txt/text file |
Introduction |
| Remove duplicate/same lines in a text file |
Introduction |
| Data labeling and annotation in supervised machine learning |
Introduction |
| Global defects and local defects identified by defect denoising |
Introduction |
| Laplacian filtering |
Introduction |
| Clustering of Laplacian |
Introduction |
| Draw circles/lines on images |
Introduction |
| Table of applications of Python and its libraries |
Introduction |
| L2 regularization/Ridge/ridge regularization/Tikhonov regularization |
Introduction |
| Support Vector Machines (SVM) and Logistic Regression |
Introduction |
| Hidden layer in deep learning neural network |
Introduction |
| Convolutional Layers (CONV) in Deep Learning |
Introduction |
| Fully Connected Layers (FC) in Deep Learning |
Introduction |
| Laplace smoothing/Laplace correction/add-one smoothing |
Introduction |
| Comparison between Poisson distribution, Gaussian (normal) distribution and logistic regression |
Introduction |
| Logistic regression versus Gaussian discriminant analysis |
Introduction |
| Joint likelihood |
Introduction |
| Cross entropy (log loss/logistic loss) |
Introduction |
| Softmax regression (multinomial logistic regression)/softmax multi-class network/softmax classifier |
Introduction |
| Canonical response function/canonical link function |
Introduction |
| Learning rule in ML |
Introduction |
| GLM (Generalized Linear Model) |
Introduction |
| Negative log likelihood (NLL) |
Introduction |
| Exponential Family: Parameter, Sufficient Statistic, Natural Parameter, Base Measure and Log-Partition Function (Bernoulli distribution and Gaussian distribution) |
Introduction |
| Perceptron algorithm and logistic regression |
Introduction |
| Update parameters θj using gradient of the loss function |
Introduction |
| Logistic function/sigmoid function |
Introduction |
| Logistic regression |
Introduction |
| Logistic regression versus linear regression |
Introduction |
| Linear regression versus classification |
Introduction |
Comparison between mean squared error (MSE), absolute error (L1 Loss) and fourth-power loss
|
Introduction |
| Comparison between L1 Regularization and L1 Loss (absolute loss or mean absolute error (MAE)) |
Introduction |
| Convolution and convolutional layers |
Introduction |
| Latent features and latent variables |
Introduction |
| Bandwidth parameter (τ) in LWR and KDE |
Introduction |
| Parametric learning algorithm |
Introduction |
| Non-parametric learning algorithm |
Introduction |
| Locally Weighted Regression (LWR) |
Introduction |
| Learning Algorithm (estimator) |
Introduction |
| Learning rate |
Introduction |
| Linear regression and its algorithm |
Introduction |
| Generalization Error/Generalization Loss/Test Error/Expected Error of Hypothesis/Risk |
Introduction |
| Lipschitzness/Lipschitz continuity |
Introduction |
| Check existence of phrase on text file line-by-line |
Introduction |
| Expected risk (population risk, expected value of loss or error) |
Introduction |
| Central Limit Theorem (CLT) |
Introduction |
| Empirical loss/training loss |
Introduction |
| Empericial loss versus population loss |
Introduction |
| Linear Discriminant Analysis |
Introduction |
| IDLE (integrated development and learning environment) and integrated development environment (IDE) |
Introduction |
| Natural Language Processing (NLP) approaches in addressing the Failure Analysis (FA) search problem |
Introduction |
| Natural Language Processing (NLP) versus Text |
Introduction |
| Bayes' theorem (Bayes rule or Bayes law) in machine learning |
Introduction |
| Analysis of papers/publications/literature in machine learning and Python applications |
Introduction |
| Multiple linear regression |
Introduction |
| Likelihood and maximum Likelihood estimation (MLE) |
Introduction |
| "Label space" in machine learning |
Introduction |
| Predicted label |
Introduction |
| "True label" ("observed label") in machine learning |
Introduction |
| Predicted label versus predictor (feature) |
Introduction |
| Loss (risk, cost, objective) function |
Introduction |
| Supervised, unsupervised and reinforcement learning |
Introduction |
| Read line-by-line from a text file |
Introduction |
| Extract the first or last N letters from a string |
Introduction |
| Nearest/most similar lyrics of a sentence/text to a CSV file |
Introduction |
| Natural language inference |
Introduction |
| NLTK (Natural Language Toolkit) |
Introduction |
| Large and small datasets in ML |
Introduction |
| (Text and image) contrastive learning |
Introduction |
| Remove/reload/unload an imported module/function/script |
Introduction |
| Access and use SQL Database on SSMS (Microsoft SQL Server Management Studio Express) with pyodbc: localhost, insert rows, update, count updated, delete rows, comparision between extract data by Python and SQL itself |
Introduction |
| Count number of lines in a text file |
Introduction |
| Pyvis: An interactive geometric graph network/link/landscape |
Introduction |
| Networkit: a network/link/landscape tool |
Introduction |
| Loop through a Python dictionary |
Introduction |
| Delete the column/row in a CSV file if they are empty or less than a number (or header/index only) |
Introduction |
| scipy.optimize.linprog function |
Introduction |
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| Plot multiple images on the same figure by hiding x- and y-(tick) labels on axis |
Introduction |
| Fairness Analysis and Python Libraries |
Introduction |
| Convert set into a list and vice versa |
Introduction |
| Lock a file to prevent deleting, and then release the file once job is done |
Introduction |
| Lock a file to prevent deleting, and then release the file once job is done |
Introduction |
| Exception LookupError (string index) |
Introduction |
| OverflowError (too large to store) |
Introduction |
Calculation with combinations of variables from lists
|
Introduction |
| Remove empty strings from list of strings |
Introduction |
| Count how many empty strings in a list |
Introduction |
| Check if two lists are same/identical |
Introduction |
| Remove the substring after the first or last character "::" in a given string, or extract the substring between the first and last "::" |
Introduction |
| Penalized regression (Lasso and Ridge) |
Introduction |
| |
|
| Compare (pattern/ratio of) two different columns, check whether column values match in DataFrame |
Introduction |
| ◆ |
Check whether one column contains number only and another column contains letters only or mixture of numbers and letters in DataFrame |
Introduction |
| ◆ |
Check the difference between two columns in DataFrame |
Introduction |
| ◆ |
|
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| |
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| Get the last line in a text file |
Introduction |
| Check if Windows/PC screen is locked |
Introduction |
| Check all the imported/current modules/libraries |
Introduction |
| Check if all the (and how many, length of a string) characters in the text are digits/numbers |
Introduction |
Different behavior of automation execution (e.g. pyautogui) locally or remotely through internet
|
Introduction |
| |
|
| Data plot with labels |
Introduction |
| Leetcode for Google/Amazon |
Introduction |
| Create a log (log.log) file to monitor script execution |
Introduction |
| Last n days/weeks/months (.to_datetime(x), .set_index(y), .last(z), .reset_index(), and .max() in pandas) |
Introduction |
| |
|
| Lists/list(). A list is a collection of objects. List is denoted with square brackets []. Declaration: myList = list().
Unlike strings, lists are mutable, a list can also contain list(s). Lists in memory are considered to be an object in memory. code1, code2: loop, code3: loop, code4: loop. mixed list and replace an item in the list. copy method. print all iterms in a list in a seperate line. data structures. General. code. |
Introduction |
| ◆ |
Check if one list is subset of another |
Introduction |
| ◆ |
Check if a list is empty or not |
Introduction |
| ◆ |
Check if an item/element is in a list or not |
Introduction |
| ◆ |
Check if a file/folder exists or not (Cannot find a specific file/folder? a specific folder in the path? select specific folders to form a string, split a dos path into its components, and then print the list, or check files with extension) |
Introduction |
| ◆ |
Check if an element is in a sublist of a list |
Introduction |
| ◆ |
Get/list immediate subdirectories/subfolders; get only the last part of a path/folder/drive; split a dos path into its components, and then print the list |
Introduction |
| ◆ |
Access elements in a list and sublist |
Introduction |
| ◆ |
Replace/substitute a item in a list |
Introduction |
| ◆ |
List all files and directories which has specific files or files with specific extensions |
Introduction |
| ◆ |
Get/list immediate subdirectories/subfolders; get only the last part of a path |
Introduction |
| ◆ |
Find minimum and maximum values in a list |
Introduction |
| ◆ |
Change/swap values in a list |
Introduction |
| ◆ |
Modify a list (e.g. add/insert an item between items) |
Introduction |
| ◆ |
Plot a list of x, y coordinates to an image |
Introduction |
| ◆ |
Sort a list |
Introduction |
| ◆ |
Split a sentence/string into list of words, remove all special characters from a sentence |
Introduction |
| ◆ |
Modify file path/directory by changing folder names by merging a list |
Introduction |
| ◆ |
Limit event/action numbers in the event List, then stop |
Introduction |
| ◆ |
Loop through a list (e.g. for loop) |
Introduction |
| ◆ |
Remove an item/element/duplicates from a list |
Introduction |
| ◆ |
Get the list of the methods for a function |
Introduction |
| ◆ |
index("") In list. |
(code) |
| ◆ |
Extract elements from a list (different way from removing elements to get part of the list) |
Introduction |
| ◆ |
Convert csv/dataframe column to a list or vice versa |
Introduction |
| ◆ |
Create dictionary from nested (sublist) list and get the values with keys |
Introduction |
| ◆ |
Convert between numpy array, string or list of string |
Introduction |
| ◆ |
Check if two lists have the same elements |
Introduction |
| ◆ |
Extract the least/most frequency/duplicate/occurrence element in a list |
Introduction |
| ◆ |
Find common/different elements/items between two lists/sets |
Introduction |
| ◆ |
Collect the file list in a folder into a csv file |
Introduction |
| ◆ |
os.listdir(). Output the file list of images in a folder, but only returns the names. code. code. code. code. (code). (code). |
Introduction |
| ◆ |
Data structures (Data science, and comparison between list, tuple, set, dictionary) |
Introduction |
| ◆ |
Split a list into columns |
Introduction |
| ◆ |
Comparison between =, ==, .copy(), copy.copy() for "list": changes of "list" |
Introduction |
| ◆ |
count() in csv/list |
Introduction |
| ◆ |
Sum a list of numbers in any length |
Introduction |
| ◆ |
Convert a list to a matrix |
Introduction |
| ◆ |
Reference list items by position |
code1, code2 |
| ◆ |
Convert/change the case of all letters/word into uppercase (capital) or lowercase in a list of strings |
Introduction |
| ◆ |
Find duplicate items in a list |
Introduction |
| |
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| Form a list of strings from an old string with all the 6 digits by removing all special characters or spaces |
Introduction |
| Plot a figure with a colored arrow between text lines/steps |
Introduction |
| __add__, __call__, __contains__, __delitem__, __delattr__, __eq__, __enter__, __ge__, __getattribute__, __getnewargs__, __getattr__, __getitem__, __gt__, __hash__, __reduce__, __iadd__, __imul__, __init_subclass__, __index__, __int__, __invert__, __new__, __neg__, __reduce_ex__, __reversed__, __rmul__, __radd__, __rand__, __rdivmod__, __rfloordiv__, __rlshift__, __rmod__, __ror__, __round__,__rpow__, __rrshift__, __rshift__, __rsub__, __rtruediv__, __rxor__, __dir__, __doc__, __divmod__, __iter__, __le__, __lt__, __len__, __ne__, __repr__, __setattr__, __setitem__, __sizeof__, __lshift__, __sub__, __subclasshook__, __str__ |
Introduction |
| Reverse a list |
Introduction |
| locals() |
Introduction |
| Convert a sentence/text to a list |
Introduction |
| Add letter/commas/numbers/characters to the end/beginning of strings in a list |
Introduction |
| Replace the lines between two lines “xx” and “yy” in a text file with new lines |
Introduction |
| Remove string 0s from the back/end of a list until non-zero values |
Introduction |
| Duplicate/repeat the same words/elements in a string/list |
Introduction |
| Extract the last column as subdataframe |
Introduction |
| Optimizing failure analysis processes in semiconductor labs using machine learning |
Introduction |
| Hide x-axis tick labels (only show some labels) where x values are under certain conditions |
Introduction |
| Font size of tick labels in plot |
Introduction |
| Cheatsheet of list |
Introduction |
| Set logarithmic scale (exponential) for y-axis in plots |
Introduction |
| Populate the table with logarithmic format in pptx |
Introduction |
| PermissionError (E.g. file cannot be written when it is open/locked) |
Introduction |
| Output data if any or same element in a string are in two lists |
Introduction |
| HTTP(Hypertext Transfer Protocol)/URL (Uniform Resource Locator) |
Introduction |
|
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| |
|
| lineterminator |
CSV: string (length 1), default None, Character to break file into lines. Only valid with C
parser |
| lineterminator |
CSV: code. |
| left_index=, right_index= |
CSV: (code) |
| .loc[] and .iloc[] |
Introduction |
| line |
CSV: Print line by line from a CSV file: code. |
Locators |
|
| driver.get("") |
|
| |
|
driver.findElement(By.linkText("NextPage")).click();
driver.findElement(By.id("")).sendKeys("")
driver.findElement(By.xpath("")).click()
driver.findElement(By.xpath("").sendKeys("")
driver.findElement(By.id(""))
chooseFile.sendKeys("")
driver.findElement(By.name("q")).sendKeys ("")
dom =document.getElementById("")
driver.FindElement(By.CssSelector(""))
driver.findElement(By.className(""))
driver.findElement(By.tagName("select")).Click()
driver.findElement(By.partialLinkText(" NextP")).click() |
|
| .locateOnScreen() |
.locateOnScreen('looksLikeThis.png') returns (left, top, width, height) on the image which the screenshot is taken from. Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match.
This function mostly is useless. Introduction. (code) |
| top and left for locateOnScreen() |
Introduction |
| .locateCenterOnScreen() |
Uses pyscreeze. x, y = MySearch_img to get the x- and y-coordinates of centers of the feature. Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match. Introduction. (code) |
| .locateAllOnScreen() |
Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match. Introduction. (code) |
| |
|
| .Label() |
Introduction |
| lambda |
Introduction. Lambda functions can be used in places where we expect variables. code. |
| class watchdog.events.LoggingEventHandler |
Bases: watchdog.events.FileSystemEventHandler
Logs all the events captured. |
| class watchdog.events.LoggingEventHandler |
Bases: watchdog.events.FileSystemEventHandler
Logs all the events captured. |
| Insert/add text or line |
Insert text or new text lines to a specific position, at the end of a line, in a file. Introduction |
| math.log(x,y) |
Returns the natural logarithm of x to base y. |
| math.log2(x) |
Returns the base-2 logarithm of x. |
| lower() |
Returns the string by converting all the characters of the string to lower case. Introduction. code. |
| LoggingEventHandler |
(code) |
| len() |
Is a function to get the length of a collection. It returns
the length of the string. Plus: The last character of a given string can also be printed. Example code. copy method. all kinds of len(). |
| |
|
| LightGBM |
Is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
Faster training speed and higher efficiency.
Lower memory usage.
Better accuracy.
Support of parallel, distributed, and GPU learning.
Capable of handling large-scale data. |
| scipy.linalg |
Linear algebra routines and matrix decompositions extending beyond those provided in numpy.linalg. scipy.linalg contains all the functions that are in numpy.linalg. Additionally, scipy.linalg also has some other advanced functions that are not in numpy.linalg. scipy.linalg operations can be applied equally to numpy.matrix or to 2D numpy.ndarray objects. code. |
| scipy.linalg.norm |
This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms, depending on the value of the ord parameter. For tensors with rank different from 1 or 2, only ord=None is supported. It is for an old release of SciPy (version 0.14.0). |
| from scipy.linalg import norm |
code |
| scipy.linalg.block_diag |
Create a block diagonal matrix from the provided arrays. |
| scipy.linalg.circulant |
Create a circulant matrix. |
| scipy.linalg.companion |
Create a companion matrix. |
| scipy.linalg.convolution_matrix |
Create a convolution matrix. |
| scipy.linalg.dft |
Create a discrete Fourier transform matrix. |
| scipy.linalg.fiedler |
Create a symmetric Fiedler matrix. |
| scipy.linalg.fiedler_companion |
Create a Fiedler companion matrix. |
| scipy.linalg.hadamard |
Create an Hadamard matrix. |
| scipy.linalg.hankel |
Create a Hankel matrix. |
| scipy.linalg.helmert |
Create a Helmert matrix. |
| scipy.linalg.hilbert |
Create a Hilbert matrix. |
| scipy.linalg.invhilbert |
Create the inverse of a Hilbert matrix. |
| scipy.linalg.leslie |
Create a Leslie matrix. |
| scipy.linalg.pascal |
Create a Pascal matrix. |
| scipy.linalg.invpascal |
Create the inverse of a Pascal matrix. |
| scipy.linalg.toeplitz |
Create a Toeplitz matrix. |
| linalg.multi_dot(arrays, *[, out]) |
Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order. |
| linalg.matrix_power(a, n) |
Raise a square matrix to the (integer) power n. |
| linalg.cholesky(a) |
Cholesky decomposition. |
| linalg.qr(a[, mode]) |
Compute the qr factorization of a matrix. |
| linalg.svd(a[, full_matrices, compute_uv, …]) |
Singular Value Decomposition. |
| linalg.norm(x[, ord, axis, keepdims]) |
Matrix or vector norm. |
| linalg.cond(x[, p]) |
Compute the condition number of a matrix. |
| linalg.det(a) |
Compute the determinant of an array. |
| linalg.matrix_rank(M[, tol, hermitian]) |
Return matrix rank of array using SVD method |
| linalg.slogdet(a) |
Compute the sign and (natural) logarithm of the determinant of an array. |
| linalg.solve(a, b) |
Solve a linear matrix equation, or system of linear scalar equations. |
| linalg.tensorsolve(a, b[, axes]) |
Solve the tensor equation a x = b for x. |
| linalg.lstsq(a, b[, rcond]) |
Return the least-squares solution to a linear matrix equation. |
| linalg.inv(a) |
Compute the (multiplicative) inverse of a matrix. code. |
| linalg.pinv(a[, rcond, hermitian]) |
Compute the (Moore-Penrose) pseudo-inverse of a matrix. |
| linalg.tensorinv(a[, ind]) |
Compute the ‘inverse’ of an N-dimensional array. |
| from scipy.linalg import eigh |
Print "selected eigenvalues" and "complex ndarray": code. |
| linalg.LinAlgError |
Generic Python-exception-derived object raised by linalg functions. |
| skimage.measure.label(input[, neighbors, ...]) |
Label connected regions of an integer array. |
| skimage.measure.LineModel() |
Total least squares estimator for 2D lines. |
| skimage.measure.LineModelND() |
Total least squares estimator for N-dimensional lines. |
| smtp.login() |
code. |
| (min_val, max_val, min_loc, max_loc) = cv2.minMaxLoc() |
Returns the max and min intensity values as an array
that includes the location of these intensities. Takes the correlation result and returns a 4-tuple which includes the minimum correlation value, the maximum correlation value, the (x, y)-coordinate of the minimum value, and the (x, y)-coordinate of the maximum value, respectively. Max_Val is the location with the highest intensity in the image, corresponding to the best matching input
image with regard to the defined template.. code. code. code. |
| cv2.line() |
Draw a line in a image. (code). code |
| |
|
| label |
Label for plot legend |
| logy |
Use logarithmic scaling on the y-axis |
| legend |
Add a subplot legend (True by default) |
| 'ls' |
code. |
| np.linspace() |
Introduction. General, code. code. |
| numpy.linalg |
This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms, depending on the value of the ord parameter. (code). |
| Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values |
CSV: Introduction. code. |
| .press(Button.left) |
(code) |
| .release(Button.left) |
(code) |
| .click(Button.left, x) |
x clicks of mouse. (code) |
| hotkey('l') |
Introduction |
| hotkey('left') |
Introduction |
| .topleft |
(code) |
| .top/.bottom/.left/.right |
Introduction |
| 'xyz'.isalpha() |
Check if string is alphabet (letter, or one type of character) |
| import logging |
(code). |
| .format.line |
(code) |
| |
|
| os.path.lexists() |
(code) |
| |
|
| .set_xticklabels() |
(code) |
