9 skills found
lequant40 / Portfolio Allocation JsA JavaScript library to allocate and optimize financial portfolios.
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
mdengler / ClaAn Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization
phschiele / PyCLAA Python Package for Portfolio Optimization using the Critical Line Algorithm
cvxgrp / Cvxclacritical line algorithm for efficient frontier
GroupAYECS765P / BDP 05 Large Scale ClusteringBDP 05: CLUSTERING OF LARGE UNLABELED DATASETS OVERVIEW Real world data is frequently unlabeled and can seem completely random. In these sort of situations, unsupervised learning techniques are a great way to find underlying patterns. This project looks at one such algorithm, KMeans clustering, which searches for boundaries separating groups of points based on their differences in some features. The goal of the project is to implement an unsupervised clustering algorithm using a distributed computing platform. You will implement this algorithm on the stack overflow user base to find different ways the community can be divided, and investigate what causes these groupings. The clustering algorithm must be designed in a way that is appropriate for data intensive parallel computing frameworks. Spark would be the primary choice for this project, but it could also be implemented in Hadoop MapReduce. Algorithm implementations from external libraries such as Spark MLib may not be utilised; the code must be original from the students. However, once the algorithm is completed, a comparison between your own results and that generated by MLlib could be interesting and aid your investigation. Stack Overflow is the main dataset for this project, but alternative datasets can be adopted after consultation with the module organiser. Additionally, different clustering algorithms may be utilised, but this must be discussed and approved y the module organiser. DATASET The project will use the Stack Overflow dataset. This dataset is located in HDFS at /data/stackoverflow The dataset for StackOverflow is a set of files containing Posts, Users, Votes, Comments, PostHistory and PostLinks. Each file contains one XML record per line. For complete schema information: Click here In order to define the clustering use case, you must define what should be the features of each post that will be used to cluster the data. Have a look at the different fields to define your use case. ALGORITHM The project will implement the k-means algorithm for clustering. This algorithm iteratively recomputes the location of k centroids (k is the number of clusters, defined beforehand), that aim to classify the data. Points are labelled to the closest centroid, with each iteration updating the centroids location based on all the points labelled with that value. Spark and Map/Reduce can be utilised for implementing this problem. Spark is recommended for this task, due to its performance benefits in . However, note that the MLib extension of Spark is not allowed to be used as the primary implementation. The group must code its own original implementation of the algorithm. However, it is possible to also use the mllib implementation, in order to evaluate the results from each clustering implementation. Report Contents Brief literature survey on clustering algorithms, including the challenges on implementing them at scale for parallel frameworks. The report should then justify the chosen algorithm (if changed) and the implementation. Definition of the project use case, where the implemented project will be part of the solution. Implementation in MapReduce or Spark of a clustering algorithm(KMeans). Must take into account the potential enormous size of the dataset, and develop sensible code that will scale and efficiently use additional computing nodes. The code will also need to potentially convert the dataset from its storage format to an in-memory representation. Source code should not be included in the report. However, the algorithms should be explained in the report. Results section. Adequate figures and tables should be used to present the results. The effectiveness of the algorithm should also be shown, including performance indications. Not really sure if this can be done for clustering. Critical evaluation of the results should be provided. Experiments demonstrating the technique can successfully group users in the dataset. Representation of the results, and discussion of the findings in a critical manner. ASSESSMENT The project according to the specification has a base difficulty of 85/100. This means that a perfect implementation and report would get a 85. Additional technical features and experimentation would raise the difficulty in order to opt for a full 100/100 mark. Report presentation: 20% Appropriate motivation for the work. Lack of typos/grammar errors, adequate format. Clear flow and style. Related work section including adequate referencing. Technical merit: 50% Completeness of the implementation. [25%] Provided source code. Code is documented. [10%] Design rationale of the code is provided. [10%] Efficient, and appropriate implementation for the chosen platform. [5%] Results/Analysis: 30% Experiments have been carried out on the full dataset. [10%] Adequate plots/tables are provided, with captions. [10%] Results are not only presented but discussed appropriately. [10%] Additional project goals: Implementation of additional functions beyond the base specification can raise the base mark up to 100. A non-exhaustive list of expansion ideas include: Exploration and discussion of hyperparameter tuning (e.g. the number of k groups to cluster the data into) [up to 10 marks] Comparative evaluation of clustering technique with existing implementations (e.g. mllib) [up to 10 marks] Bringing in additional datasets from stackoverflow, such as user badges, to aid in clustering [up to 5 marks] Cluster additional datasets (such as posts) [up to 10 marks] LEAD DEMONSTRATOR For specific queries related to this coursework topic, please liaise with Mr/Ms TBD, who will be the lead demonstrator for this project, as well as with the module organiser. SUBMISSION GUIDELINES The report will have a maximum length of 8 pages, not counting cover page and table of contents. The report must include motivation of the problem, brief literature survey, explanation of the selected technique, implementation details and discussion of the obtained results, and references used in the work. Additionally, the source code must be included as a separate compressed file in the submission.
ungil / Markowitz.jlMean-variance optimization using the critical line algorithm
PharosAbad / EfficientFrontier.jlEntire Efficient Frontier by Status-Segment Method
KhiM26 / Latest[i]Default attack speed is lowered compared to Minecraft. You can land one blow in 1.3 seconds with 0 Agility.[/i] This mod should be single player and multiplayer compatible. [size=large][b]Support further development! [/b]:Diamond:[/size] [url="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=HNL62YQZ57P7E"][img]https://www.paypalobjects.com/en_US/RU/i/btn/btn_donateCC_LG.gif[/img][/url] [size=large][b]How to Install[/b][/size] 1. If you haven't already, download and install Minecraft Forge: [url="http://www.minecraftforge.net/wiki/Installation/Universal"]http://www.minecraft...ation/Universal[/url] 2. Download the Dota 2 Items mod:[list] [*][b]v0.8.3.12 [/b]for Minecraft v1.6.x: [url="http://adf.ly/cemsY"]http://adf.ly/cemsY[/url] [*][i][b]older version v0.5.3[/b][/i] for Minecraft v1.5.2: [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/dota2items-v0.5.3.zip"]http://adf.ly/455455...-v053-for-mc152[/url] [/list] 3. Download the required LibSchematic mod:[list] [*]for Minecraft v1.6.x: [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/libschematic-v1.03.zip"]https://dl.dropboxus...matic-v1.03.zip[/url] [/list] 4. Copy both zip files into your .minecraft/mods folder [b][size=large]Items So Far[/size][/b] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/clarity.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/clarity.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/tango.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/tango.png[/img][/url] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/healingSalve.png[/img] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ironBranch.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ironBranch.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/gauntletsOfStrength.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/gauntletsOfStrength.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/slippersOfAgility.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/slippersOfAgility.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/mantleOfIntelligence.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/mantleOfIntelligence.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/circlet.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/circlet.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/beltOfStrength.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/beltOfStrength.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bandOfElvenskin.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bandOfElvenskin.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/robeOfTheMagi.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/robeOfTheMagi.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ogreClub.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ogreClub.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bladeOfAlacrity.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bladeOfAlacrity.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/staffOfWizardry.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/staffOfWizardry.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ultimateOrb.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ultimateOrb.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ringOfProtection.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ringOfProtection.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/quellingBlade.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/quellingBlade.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/stoutShield.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/stoutShield.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bladesOfAttack.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bladesOfAttack.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/chainmail.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/chainmail.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/helmOfIronWill.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/helmOfIronWill.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/broadsword.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/broadsword.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/quarterstaff.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/quarterstaff.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/claymore.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/claymore.png[/img][/url] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/javelin.png[/img] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/platemail.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/platemail.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/mithrilHammer.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/mithrilHammer.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/sagesMask.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/sagesMask.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ringOfRegen.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ringOfRegen.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bootsOfSpeed.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bootsOfSpeed.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/glovesOfHaste.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/glovesOfHaste.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/cloak.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/cloak.png[/img][/url] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/morbidMask.png[/img] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/talismanOfEvasion.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/talismanOfEvasion.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/blinkDagger.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/blinkDagger.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/wraithBand.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/wraithBand.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/nullTalisman.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/nullTalisman.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bracer.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/bracer.png[/img][/url] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/poorMansShield.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/phaseBoots.png[/img] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/oblivionStaff.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/oblivionStaff.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/perseverance.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/perseverance.png[/img][/url] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/handOfMidas.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/arcaneBoots.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/forceStaff.png[/img] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/eulsScepter.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/eulsScepter.png[/img][/url] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/dagon.png[/img] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/aghanimsScepter.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/aghanimsScepter.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/crystalys.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/crystalys.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/daedalus.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/daedalus.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/butterfly.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/butterfly.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/divineRapier.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/divineRapier.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/hoodOfDefiance.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/hoodOfDefiance.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/vanguard.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/vanguard.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/soulBooster.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/soulBooster.png[/img][/url] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/maskOfMadness.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/yasha.png[/img] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ringOfHealth.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/ringOfHealth.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/voidStone.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/voidStone.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/vitalityBooster.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/vitalityBooster.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/energyBooster.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/energyBooster.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/pointBooster.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/pointBooster.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/hyperstone.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/hyperstone.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/reaver.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/reaver.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/eaglesong.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/eaglesong.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/mysticStaff.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/mysticStaff.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/demonEdge.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/demonEdge.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/sacredRelic.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/sacredRelic.png[/img][/url] [url="https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/recipe.png"][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/16/recipe.png[/img][/url] [spoiler]I started with items with effects that would be both very useful in Minecraft while not too easy or too difficult to implement. For now they are only available in the creative inventory, in a special tab. Also in creative mode all items have zero cooldown. [hr] [img]https://dl.dropbox.com/u/6746449/Dota2Items/BlinkDaggerSmall.png[/img] [i]Possibly the most comfortable way to travel in creative mode.[/i] [b]Active: [/b]Blink - Teleport to a target point up to 30 meters away. If damage is taken from an enemy, Blink Dagger cannot be used for 3 seconds. In Minecraft, Blink negates all fall damage and lands you on a surface (land or water or also lava, so be careful). If you target 2 blocks below the top of a cliff or a wall, you will teleport on top of it. [b]Mana cost: [/b]75 [b]Cooldown: [/b]14 seconds [b]Gold cost: [/b]2150 [hr] [img]https://dl.dropbox.com/u/6746449/Dota2Items/TangoPosterSmall.png[/img] [i]Possibly the quickest way to cut down a whole jungle, lol.[/i] [b]Use:[/b] Eat Tree - Consume a tree to restore HP over time. Comes with 3 charges.[i]Consumes 1 Tango.[/i][i]The tree must have a trunk at least 3 blocks high.[/i] [b]Duration: [/b]16 seconds. [b]Gold cost: [/b]90 [hr] [img]https://dl.dropbox.com/u/6746449/Dota2Items/QuellingBladePosterSmall.png[/img] [b]Active:[/b] Destroy Tree - Destroy a target tree. [b]Cooldown:[/b] 5 seconds [b]Passive:[/b] Quell - Gives bonus attack damage (currently against all mobs), depending on the type of the attack. [b]Melee bonus:[/b] 32% [b]Ranged bonus:[/b] 12% [b]Gold cost: [/b]225 [hr] [img]https://dl.dropbox.com/u/6746449/Dota2Items/EulPosterSmall.png[/img] [b]Active:[/b] Cyclone - Target unit is swept up in a cyclone for 2.5 seconds, and is invulnerable. Target also cannot move, attack or use items. [i]Use right click to activate Cyclone on an a mob; also you can right-click the block on which the mob is standing. You can target yourself that way.[/i] [b]Mana cost:[/b] 75 [b]Cooldown:[/b] 30 seconds [b]Passive: [/b]+30 Movement Speed, +10 Intelligence (every point of Intelligence in turn gives you 13 mana and 0.04 mana regeneration) [b]Total gold cost:[/b] 2800 [hr] There are too many items already to list them all here, just read their description in the mod at the Dota 2 Shop.[/spoiler] [size=large][b]Screenshots[/b][/size] [spoiler][img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/screenshots/2013-09-01_02.41.00.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/screenshots/2013-07-01_02.13.26.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/screenshots/2013-09-01_02.42.49.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/screenshots/2013-09-25_06.39.51.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/screenshots/arcane_boots_and_tango.png[/img] [img]https://dl.dropboxusercontent.com/u/6746449/Dota2Items/screenshots/clarity_midas_phase_effect.png[/img] [size=medium][/spoiler][/size] [b][size=large]Quick Video Preview[/size][/b] [spoiler][media]http://www.youtube.com/embed/o54BAiOMa-8[/media][/spoiler] [size=large][b]Longer Video[/b][/size] [spoiler][media]http://www.youtube.com/embed/ecIs5C0hlqU[/media][/spoiler] [b][size=large]Change Log[/size][/b] [spoiler][b]v0.8.12][/b][list] [*]Fixed Hand of Midas crashing in SMP, Force Staff not working, gold not awarded for killing mobs. [/list] [b]v0.8.3[/b][list] [*]Added particle effects to Clarity, Hand of Midas, Phase Boots [/list] [b]v0.8.2[/b][list] [*]Bug fixes. [*]Added particle effects to Arcane Boots and Tango. [/list] [b]v0.8.1[/b][list] [*]Bug fixes. [*]Debug logging is now toggled in config. [/list] [b]v0.8[/b][list] [*]Bug fixes. [*]Added Force Staff and Dagon. [*]All active items have longer range and work on right click. Right-click on block below yourself to self-cast. [*]Improved Blink Dagger mechanics. [*]Improved shop GUI to allow traversal of recipe hierarchy. [/list] [b]v0.7[/b][list] [*]Bug fixes for mob movement speed and cancelled attacks. [*]Added active buffs and debuffs icons. [*]Added crit sounds. [*]Added particle effect for lifesteal and evasion. [*]Added Morbid Mask, Mask of Madness, Javelin, Arcane Boots, Phase Boots, Healing Salve, Poor Man's Shield. [*]Made attack animation look swifter, sharper. [*]Active items are tinted blue when you don't have enough mana. [/list] [b]v 0.6.4[/b][list] [*]Improved Shop positioning: ground is elevated. [/list] [b]v 0.6.3[/b][list] [*]Gold is added directly to the player. Kill assists should work. Gold is internally separated into reliable and unreliable pile. [*]Attributes are increased when leveling up. [*]Shopkeeper spawns in a shop tent outside the village. Added a spawner egg for him. [*]Added Hand of Midas and Yasha. [*]Lowered base attack time to 1.3 seconds. [*]Golems and bosses are immune to magic. [*]Bug fixes. [/list] [b]v 0.6.2[/b][list] [*]Fixed bug with Daedalus attacks being cancelled. [/list] [b]v 0.6.1[/b][list] [*]Added Clarity, Aghanim's Scepter, Hood of Defiance, Crystalys, Daedalus (works as a crossbow) [*]Swing animation now corresponds to Dota attack time. Bow charge rate is also affected by it. [*]Hopefully fixed crash in Shopkeeper gui (method drawHoveringText not found) [/list] [b]v 0.6[/b][list] [*]Updated to Minecraft v1.6.2 [/list] [b]v 0.5.3[/b][list] [*]Added 16 more passive items. [/list] [b]v 0.5.2[/b][list] [*]Implemented Evasion, Critical Strike, Spell Resistance, Magic Amplification, Damage Block [*]Added a bunch more simple items. [/list] [b]v 0.5.1[/b][list] [*]Added scrolling to shop [*]Added all items from the Attributes column [/list] [b]v 0.5[/b][list] [*]Added the Shopkeeper. [*]Added gold. [*]Added item crafting. [*]Added functioning attributes (HP, Mana, Stength, Intelligence, Agility etc). [*]Added Boots of Speed, Ring of Protection, Sage's Mask, Staff of Wizardry, Void Stone. [*]Fixed lots of issues. [/list] [b]v 0.4.1[/b][list] [*]Fixed an issue with particle textures. [*]Tested multiplayer. [/list] [b]v 0.4.0[/b][list] [*]Updated to Minecraft version 1.5.2. Multiplayer not tested yet, but it should work. [/list] [b]v 0.3.0[/b][list] [*]added Eul's Scepter of Divinity. Pretty buggy and incomplete though. [*]added mcmod.info [/list] [b]v 0.2.3[/b][list] [*]probably fixed the Blink cooldown issue (when you click a moment before the end of the cooldown, you get cooldown but no Blink) [/list] [b]v 0.2.2[/b][list] [*]minor improvements, bug fixes (fixed Blink on double slabs) [/list] [b]v 0.2.1[/b][list] [*]bug fixes [/list] [b]v 0.2.0[/b][list] [*]added Quelling Blade [*]from now on Dota 2 items do not drop on death and are retained on respawn [*]adjusted tree removal algorithm (still far from perfect) [*]fixed Blink on fences and slabs [/list] [b]v 0.1.2[/b][list] [*]added Blink Dagger [*]added Tango [/list] [/spoiler] [b][size=large]Known Bugs[/size][/b][spoiler][list] [*]Tango and Quelling Blade sometimes leave blocks of leaves in the air, or remove other trees' branches, or cleave nearby bushes. [/list] [/spoiler] [b]Additional credit: [/b]Shop tent by Aelous, LibSchematic by tehbeard ([url="https://github.com/tehbeard/LibSchematic"]https://github.com/t...rd/LibSchematic[/url]) Planet Minecraft link: [url="http://www.planetminecraft.com/mod/147forgedota-2-items-modwip/"]http://www.planetmin...2-items-modwip/[/url] This mod is on GitHub: [url="https://github.com/Hunternif/Dota2Items"]https://github.com/H...rnif/Dota2Items[/url] [i]If you encounter a bug that is not known yet, please do post it here and surround the crash report with [spoiler] and [code] tags.[/i] Must not have more than 10000 characters on a single line.
