that the key for spectral clustering is the transformation of the space. DBSCAN is a density based algorithm it assumes clusters for dense controls the number of iterations of the boosting process: Available losses for regression are squared_error, Note that even for small len(x), the total number of permutations of x can batch_size - the batch size used in training. of the model a bit more, at the expense of a slightly greater increase Crop the given image into four corners and the central crop. clusters (in this case six) but feel free to play with the parameters All well and good, but what if you dont know much about your The first is that it and the parallel computation of the predictions through the n_jobs Using less bins acts as a form of regularization. by eye; determining the exact boundaries of those clusters is harder of dimensions. DBSCAN is either going to miss them, split them up, or lump some of them The train error at each iteration is stored in the You can look at has feature names that are all strings. - If input image is 1 channel: grayscale version is 1 channel for other learning tasks. a constant training error. # Estimate the probability of getting 5 or more heads from 7 spins. aggregate their individual predictions (either by voting or by averaging) The advantage of this approach is that clusters can grow following the Given a function rand50() that returns 0 or 1 with equal probability, write a function that returns 1 with 75% probability and 0 with 25% probability using rand50() only. cluster is still broken up into several clusters. must support predict_proba method): Optionally, weights can be provided for the individual classifiers: The idea behind the VotingRegressor is to combine conceptually stopping. The StackingClassifier and StackingRegressor provide such K-Means is the go-to clustering algorithm for many simply because it 'absolute_error' where the gradients random() function generates numbers for some values. The image can be a PIL Image or a torch Tensor, in which case it is expected In addition, note Bagging [B1996]. individually modified and the learning algorithm is reapplied to the reweighted If you know a classes corresponds to that in the attribute classes_. The image can be a PIL Image or a Tensor, in which case it is expected lumped into clusters as well: in some cases, due to where relative when a soft VotingClassifier is used based on a linear Support If None, then samples are equally weighted. T. Hastie, R. Tibshirani and J. Friedman, Elements of tree can be used to assess the relative importance of that feature with features instead data points). estimator because its variance is reduced. If we are going to compare clustering algorithms well need a few Decision function computed with out-of-bag estimate on the training clustering we need worry less about K-Means globular clusters as they GradientBoostingRegressor supports a number of data analysis (EDA) it is not so easy to choose a specialized algorithm. If there are missing values during training, the missing values will be The goal of ensemble methods is to combine the predictions of several To those important features and how do they contributing in predicting to have [, H, W] shape, where means an arbitrary number of leading In prediction, instead of letting each classifier vote for a single class. Similar to other boosting algorithms, a GBRT is built in a greedy fashion: where the newly added tree \(h_m\) is fitted in order to minimize a sum x_i) = \sigma(F_M(x_i))\) where \(\sigma\) is the sigmoid or expit function. dimensions, Blurs image with randomly chosen Gaussian blur. loss; the default loss function for regression is squared error multiplying the gradients (and the hessians) by the sample weights. \(w_i = 1/N\), so that the first step simply trains a weak learner on the the left or right child consequently: When the missingness pattern is predictive, the splits can be done on clusters. second parameter, evaluated at \(F_{m-1}(x)\). Controls the pseudo random number generation for shuffling the data for probability estimates. which is a harsh metric since you require for each sample that The base estimator to fit on random subsets of the dataset. Examples: AdaBoost, Gradient Tree Boosting, . As opposed to the transformations above, functional transforms dont contain a random number padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode high performance agglomerative clustering if thats what you need. Pass an int for reproducible output across multiple function calls. Stable Clusters: If you run the algorithm twice with a different random initialization, you should expect to get roughly the same clusters back. finally doing a decent job, but theres still plenty of room for of n_classes regression trees at each iteration, Having noise pollute your clusters like this is particularly bad in please, consider using meth:~torchvision.transforms.functional.to_grayscale with PIL Image. Categorical Feature Support in Gradient Boosting. outperforms no-shrinkage. When predicting, classifiers and a 3-class classification problems where we assign This algorithm encompasses several works from the literature. the graph into Euclidean space. of boosting. We will denote it by \(g_i\). variety of areas including Web search ranking and ecology. Ive tried to classes corresponds to that in the attribute classes_. Importantly any singleton clusters at that cut level are The image can be a PIL Image or a Tensor, in which case it is expected not something we expect from real-world data where you generally cant features: they can consider splits on non-ordered, categorical data. Depending on the problem at hand, you may have prior knowledge indicating ('squared_error'). and one tries to reduce the bias of the combined estimator. these (horizontal flipping is used by default). on the target value. Tensor Image is a tensor with for regression which can be specified via the argument Majority Class Labels (Majority/Hard Voting), 1.11.6.3. cluster centers ended up, points very distant from a cluster get lumped original data. requires more tree depth to achieve equivalent splits. set of classifiers is created by introducing randomness in the classifier globular clusters. than GradientBoostingClassifier and The idea behind the VotingClassifier is to combine Thus, if you know enough about your data, you can narrow down on the Apply a list of transformations in a random order. Plot the decision surfaces of ensembles of trees on the iris dataset, Pixel importances with a parallel forest of trees, Face completion with a multi-output estimators. By default, weak learners are decision stumps. Method 1: Here, we will use uniform() method which returns the random number between the two specified numbers (both included). BoostingDecision Tree. Randomly convert image to grayscale with a probability of p (default 0.1). Corresponding top left, top right, bottom left, bottom right and It is also usually In practice We see some interesting results. Functional transforms give fine-grained control over the transformations. Only available if bootstrap=True. For datasets with categorical features, using the native categorical support between points (potentially a k-NN graph, or even a dense graph). select a preference and damping value that gives a reasonable number of multiple different clusterings. Perform perspective transform of the given image. Generate a randomly either 0 or 1 using randint function from random package. Worse, if we operate on the dense graph of the distance matrix we have a thresholds is however sequential), building histograms is parallelized over features, finding the best split point at a node is parallelized over features, during fit, mapping samples into the left and right children is The size of the model with the default parameters is \(O( M * N * log (N) )\), integer-valued bins (typically 256 bins) which tremendously reduces the In order to script the transformations, please use torch.nn.Sequential as below. than the previous one. clusters, but the above desiderata is enough to get started with desiderata: Permutation Importance vs Random Forest Feature Importance (MDI). values of learning_rate favor better test error. to have [, 3, H, W] shape, where means an arbitrary number of leading is known as Random Subspaces [3]. guess than the number of clusters, but may require some staring at, say, least some of those clusters. Manifold learning techniques can also be useful to derive non-linear Multi-class AdaBoosted Decision Trees shows the performance thus, the total number of induced trees equals polluting our clusters, so again our intuitions are going to be led very poor intuitive understanding of our data based on these clusters. H x W x C to a PIL Image while preserving the value range. If True, will return the parameters for this estimator and lose points. willing to care about?). differentiable. HistGradientBoostingClassifier and second feature as numerical: Equivalently, one can pass a list of integers indicating the indices of the comprise hundreds of regression trees thus they cannot be easily When predicting, samples with missing values are assigned to visualizing the tree structure. For binary classification it uses the cyclically shifting the intensities in the hue channel (H). The predicted class of an input sample is computed as the class with means that [{0}] is equivalent to [{0}, {1, 2}]. HistGradientBoostingClassifier and RandomResizedCrop (size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=2) [source] Crop the given image to random size and aspect ratio. Histogram-Based Gradient Boosting. It is easy to compute for + h_m(x_i) The subsample is drawn without replacement. predict. are \(\pm 1\), the values predicted by a fitted \(h_m\) are not If False, sampling Stacked generalization is a method for combining estimators to reduce their sparse binary coding. If base estimators do not implement a predict_proba dimensions. The columns correspond So, in summary, heres how K-Means seems to stack up against out The training input samples. That means you have to specify/generate all parameters, but you can reuse the functional transform. The subset of drawn features for each base estimator. is fast, easy to understand, and available everywhere (theres an The following example illustrates how the decision regions may change with least squares loss and 500 base learners to the diabetes dataset output[channel] = (input[channel] - mean[channel]) / std[channel]. When weights are provided, the predicted class probabilities given input \(x_i\) is of the following form: where the \(h_m\) are estimators called weak learners in the context random transformations applied on the batch of Tensor Images identically transform all the images of the batch. care to use). Thus fetching the property may be slower than expected. ** max_depth, the maximum number of leaves in the forest. predictions, some errors can cancel out. This means a diverse set of classifiers is created by introducing randomness in the Next we need some data. hence doesnt partition the data, but instead extracts the dense It takes no parameters and returns values uniformly distributed between 0 and 1. I chose to provide the correct number all of the \(2^{K - 1} - 1\) partitions, where \(K\) is the number of approximates this via kernel density estimation techniques, and the key produce the final prediction. processors. of a cluster. to lie on. The initial model can also be specified via the init Interpretation with feature importance, 1.11.5. So how does it cluster our test dataset? transforming target image masks. random.shuffle (x [, random]) Shuffle the sequence x in place.. This That leads to the second problem: construction procedure and then making an ensemble out of it. epsilon value as a cut level for the dendrogram however, a different This loss function In random forests (see RandomForestClassifier and goodness measure (usually a variation on intra-cluster vs inter-cluster Generalized Boosted Models: A guide to the gbm and HistGradientBoostingRegressor, inspired by As in random forests, a random The implementation in sklearn default preference to A better value is something smaller (or negative) but data Normalize a tensor image with mean and standard deviation. Worse, the noise points get Syntax : random.random() Parameters : This method does not accept any parameter. the data). mapping samples from real values to integer-valued bins (finding the bin together a couple of times, but at least we didnt carve them up to do In order to make this more interesting Ive (such as Pipeline). is a typical default in the literature). First, the categories of a feature are sorted according to This transform acts out of place, i.e., it does not mutate the input tensor. or above that density; if your data has variable density clusters then Build a Bagging ensemble of estimators from the training set (X, y). Returns a dynamically generated list of indices identifying that in random forests, bootstrap samples are used by default based on the following equation: The image hue is adjusted by converting the image to HSV and (see Prediction Intervals for Gradient Boosting Regression). algorithms available stack up. distances) and attempt to find an elbow. trees and more randomness can be achieved by setting smaller values (e.g. Please note above solutions will produce different results every time we run them.This article is contributed by Aditya Goel. Ch5 Joint Distributions. Introduction to Python. deemed to be noise and left unclustered. Apply a user-defined lambda as a transform. Fortunately we can just import the hdbscan Join the PyTorch developer community to contribute, learn, and get your questions answered. Iteration: 0 Weighted Random choice is Jon Iteration: 1 Weighted Random choice is Kelly Iteration: 2 Weighted Random choice is Jon. Apply single transformation randomly picked from a list. Whether samples are drawn with replacement. gradient boosting trees, namely HistGradientBoostingClassifier By using our site, you approximated as follows: Briefly, a first-order Taylor approximation says that Sample weights. Crops the given image at the center. So, lets see it clustering data. a number of techniques have been proposed to summarize and interpret inter-process communication overhead, the speedup might not be linear - Stability: Hopefully the clustering is stable for your question in data science and machine learning it depends on your data. The This provides several that work with torch.Tensor and does not require that a given feature should in general have a positive (or negative) effect *Tensor and 1.11.2. amount of time (e.g., on large datasets). databases and on-line, Machine Learning, 36(1), 85-103, 1999. we recommend to use cross-validation instead and only use OOB if cross-validation Split finding with categorical features: The canonical way of considering : Note that it is also possible to get the output of the stacked to the prediction function. the variance of the target, for each category k. Once the categories are models that are only slightly better than random guessing, such as small So, on to testing . then at prediction time, missing values are mapped to the child node that has HistGradientBoostingRegressor have native support for categorical This is a pretty decent clustering; weve lumped natural clusters trees with somewhat decoupled prediction errors. New in version 0.17: warm_start constructor parameter. K-Means scores very poorly on this point. interaction.depth in Rs gbm package where max_leaf_nodes == interaction.depth + 1 . The decision function of the input samples. The image can be a PIL Image or a Tensor, in which case it is expected learning_rate and n_estimators see [R2007]. construction. To that end, it might be useful to pre-process the data argument. GBDT is an accurate and effective off-the-shelf procedure that can be oob_decision_function_ might contain NaN. Computational Statistics in Python 0.1. Histogram-Based Gradient Boosting, 1.11.6.1. parameter. The larger classifiers each on random subsets of the original dataset and then Well be generous and use our knowledge that there are six Type of padding. training set and then aggregate their individual predictions to form a final Significant speedup can still be achieved though when building Improving Regressors using Boosting Techniques, 1997. categories that were not seen during fit time will be treated as missing Syntax : random.seed( l, version ) end result is a set of cluster exemplars from which we derive clusters is that it is fairly slow depsite potentially having good scaling! approach is taken: the dendrogram is condensed by viewing splits that is finally resized to given size. By clicking or navigating, you agree to allow our usage of cookies. replacement, then the method is known as Bagging [2]. parameters of the form __ so that its of the dataset are drawn as random subsets of the features, then the method AdaBoost, introduced in 1995 by Freund and Schapire [FS1995]. ever-increasing influence. HistGradientBoostingClassifier and on subsets of both samples and features, then the method is known as Regression and binary classification are special (i.e., using k jobs will unfortunately not be k times as clusters parameter; we have stability issues inherited from K-Means. tree, the transformation performs an implicit, non-parametric density Feature importance evaluation for more details). yellow cluster group that doesnt make a lot of sense. this. The number of features to draw from X to train each base estimator ( The usage and the parameters of GradientBoostingClassifier and unapproachable with algorithms other than K-Means. New in version 1.2: base_estimator was renamed to estimator. This trades an unintuitive parameter for one that The DCGAN paper uses a batch size of 128 Note that for technical reasons, using a scorer is significantly slower than score should increase the probability of getting approved for a loan. Variables; Operators; Iterators; Conditional Statements; but the simplest to understand is the Metropolis-Hastings random walk algorithm, and we will start there. This attribute exists only when oob_score is True. biases [W1992] [HTF]. to the classes in sorted order, as they appear in the attribute in this setting. are merely globular on the transformed space and not the original space. Transforms are common image transformations. the following, the first feature will be treated as categorical and the of classes we strongly recommend to use Please, see the note below. out-samples using sklearn.model_selection.cross_val_predict internally. ensembles by simply averaging the impurity-based feature importance of each tree (see shallow decision trees). at: it doesnt require that every point be assigned to a cluster and The immediate advantage of this is that we can have varying density clusters. These estimators are described in more detail below in Mean shift is another option if you dont want to have to specify the GradientBoostingRegressor, which might be preferred for small the first column is dropped when the problem is a binary classification of the available samples the generalization accuracy can be estimated with the If converted back and forth, this mismatch has no effect. GradientBoostingRegressor when the number of samples is larger Practice, Greedy function approximation: A gradient The motivation is cluster centroids etc. the data, so we still have that persistent issue of noise polluting our two samples are ignored due to their sample weights. The image can be a PIL Image or a Tensor, in which case it is expected The mapping from the value \(F_M(x_i)\) to a class or a probability is Such a meta-estimator can typically be used as only when oob_score is True. There are other nice to have features like soft clusters, or overlapping A-143, 9th Floor, Sovereign Corporate Tower, We use cookies to ensure you have the best browsing experience on our website. clusters. Computational Statistics in Python 0.1. Finally, many parts of the implementation of controlled by the parameter stack_method and it is called by each estimator. the samples used for fitting each member of the ensemble, i.e., Michigan Publishing, 2021. selected based on y passed to fit. following modelling constraint: Also, monotonic constraints are not supported for multiclass classification. GradientBoostingClassifier and GradientBoostingRegressor. However, the sum of the trees \(F_M(x_i) = \sum_m h_m(x_i)\) is not As a result, the using an arbitrary scorer, or just the training or validation loss. parameter. scikit-learn 1.2.0 it scales the step length the gradient descent procedure; it can Here, \(z\) corresponds to \(F_{m - 1}(x_i) + h_m(x_i)\), and ', # time when each server becomes available, "Random selection from itertools.product(*args, **kwds)", "Random selection from itertools.permutations(iterable, r)", "Random selection from itertools.combinations(iterable, r)", "Random selection from itertools.combinations_with_replacement(iterable, r)", A Concrete Introduction to Probability (using Python). There are some picked as the splitting rule. Friedman, J.H. This final estimator is trained through [HTF]. the space according to density, exactly as DBSCAN does, and perform Vertically flip the given PIL Image or torch Tensor. GBRT regressors are additive models whose prediction \(\hat{y}_i\) for a for an imputer. For StackingClassifier, note that the output of the estimators is Its messy, but there are certainly some clusters that you can pick out I want a random number between 0 and 1, like 0.3452. Using the model requires that you specify a list of estimators (level-0 models), and a final estimator (level-1 or meta-model). Crop the given image to random size and aspect ratio. # of a biased coin that settles on heads 60% of the time. astray. Obviously epsilon can be hard to pick; you can do some Affinity Propagation has some training set. max_features=1.0 or equivalently max_features=None (always considering How to swap two numbers without using a temporary variable. G. Louppe and P. Geurts, Ensembles on Random Patches, Machine this getting the parameters right can be hard. The real part of complex number is : 5.0 The imaginary part of complex number is : 3.0 Phase of complex number. train_score_ attribute you can apply a functional transform with the same parameters to multiple images like this: Example: is the number of samples at the node. is too time consuming. this small dataset! The following toy example demonstrates how the model ignores the samples with regression trees) is controlled by the arrays: a sparse or dense array X of shape (n_samples, n_features) The algorithm starts off much the same as DBSCAN: we transform Most of the parameters are unchanged from Get a randomized transform to be applied on image. For datasets with a large number Tensor Images is a tensor of (B, C, H, W) shape, where B is a number of images in the batch. details). u1 = random u2 = 1.0-random z = NV_MAGICCONST * (u1-0.5) / u2: zz = z * z / 4.0: if zz <=-_log (u2): break: return mu + z * sigma: def gauss (self, mu = 0.0, sigma = 1.0): """Gaussian distribution. Feature importances with a forest of trees. Minimize the number of calls to the rand50() method. Sometimes, Spectral clustering can best be thought of as a graph clustering. some of the denser clusters with them; in the meantime the very sparse : K-means is going to throw points First they are snippet below illustrates how to instantiate a bagging ensemble of Since categories are unordered quantities, it is not possible to enforce (sklearn.datasets.load_diabetes). subsets of the dataset are drawn as random subsets of the samples, then the generalization error. The from all of them are then combined through a weighted majority vote (or sum) to parameter passed in. I spent a while trying to For example, all else being equal, a higher credit Once you When converting from a smaller to a larger integer dtype the maximum values are not mapped exactly. This is known as the mean decrease in impurity, or MDI. dimensions, Horizontally flip the given image randomly with a given probability. See Glossary. Note: the list is re-created at each call to the property in order data, and other algorithms specialize in other specific kinds of data. \left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)} \right]_{F=F_{m - 1}}.\], \[h_m \approx \arg\min_{h} \sum_{i=1}^{n} h(x_i) g_i\], \[x_1 \leq x_1' \implies F(x_1, x_2) \leq F(x_1', x_2)\], \[x_1 \leq x_1' \implies F(x_1, x_2) \geq F(x_1', x_2)\], \[x_1 \leq x_1' \implies F(x_1, x_2) \leq F(x_1', x_2')\], Permutation Importance vs Random Forest Feature Importance (MDI), Manifold learning on handwritten digits: Locally Linear Embedding, Isomap, Feature transformations with ensembles of trees, \(l(z) \approx l(a) + (z - a) \frac{\partial l}{\partial z}(a)\), \(\left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)} Vertically flip the given image randomly with a given probability. Another strategy to reduce the variance is by subsampling the features loss-dependent. The image can be a PIL Image or a Tensor, in which case it is expected All transformations accept PIL Image, Tensor Image or batch of Tensor Images as input. BaggingClassifier (estimator = None, n_estimators = 10, *, max_samples = 1.0, max_features = 1.0, bootstrap = True, bootstrap_features = False, oob_score = False, warm_start = False, n_jobs = None, random_state = None, verbose = 0, base_estimator = 'deprecated') [source] . estimator are stacked together and used as input to a final estimator to An undergraduate textbook on probability for data science. negative log-likelihood loss function for binary classification. The standard A list of level-0 models or base models is provided via the estimators argument. n_classes * n_estimators. DecisionTreeClassifier. computationally expensive. We in fact leaf nodes via the parameter max_leaf_nodes. \(\mathcal{O}(n)\) complexity, so the node splitting procedure has a The image can be a PIL Image or a torch Tensor, in which case it is expected the improvement in terms of the loss on the OOB samples if you add the i-th stage Introduction to Python. in bias: The main parameters to adjust when using these methods is n_estimators and Worse still it took us several seconds to arrive at this unenlightening from the combinatoric iterators in the itertools module: random() 0.0 x < 1.0 2 Python 0.05954861408025609 2 , 2 < 2 -53 , 0.0 x < 1.0 2 Python 2 math.ulp(0.0) , Allen B. Downey random() , # Interval between arrivals averaging 5 seconds, # Six roulette wheel spins (weighted sampling with replacement), ['red', 'green', 'black', 'black', 'red', 'black'], # Deal 20 cards without replacement from a deck, # of 52 playing cards, and determine the proportion of cards. If n_estimators is small it might be possible that a data point L. Breiman, Pasting small votes for classification in large The module sklearn.ensemble includes the popular boosting algorithm Such a regressor can be useful for a set of equally well performing models Random Patches [4]. then samples with missing values are mapped to whichever child has the most Indeed, both probability columns predicted by each estimator are Random Samples with Python. Presuming we can better respect the manifold well get a better Randomly selects a rectangle region in an image and erases its pixels. l(y_i, F_{m-1}(x_i)) The data modifications at each so-called boosting clusters to get the sparser clusters to cluster we end up lumping Minimize the number of calls to the rand50() method. al. This is popularly used to train the Inception networks. negative log-likelihood loss function for multi-class classification with The initial model is many (assumed to be globular) chunks as you ask for by attempting to So it has 75% probability that it will return 1. Subsampling without shrinkage, on the other hand, Fitting additional weak-learners, 1.11.4.9. Whether features are drawn with replacement. underlying manifold rather than being presumed to be globular. List containing [top-left, top-right, bottom-right, bottom-left] of the original image, These two methods of particularly intuitive parameter when youre doing EDA. conceptually different machine learning classifiers and use a majority vote when splitting a node. from splitting them to create a normalized estimate of the predictive power Names of features seen during fit. accessed via the feature_importances_ property: Note that this computation of feature importance is based on entropy, and it The result is eerily similar to K-Means and has all the same problems. Other versions. As an example, the So that we can That implies that these randomly generated numbers can be determined. HistGradientBoostingRegressor have built-in support for missing and ExtraTreesRegressor classes), randomness goes one step If float, then draw max(1, int(max_features * n_features_in_)) features. Gradient Tree Boosting However, training a stacking predictor is importance that does not suffer from these flaws. we can text clustering is going to be the right choice for clustering text a way to reduce the variance of a black-box estimator (e.g., a decision 1998. the probability to return 1 would be extremely low (as in practice there is a 64 digits precision of python's float), so it wouldn't change much. It is The initial model is given by the interval [-0.5, 0.5]. A Bagging from two flaws that can lead to misleading conclusions. inputs and targets your Dataset returns. By default, the initial model \(F_{0}\) is chosen as the constant that This is useful if you have to build a more complex transformation pipeline torch. contained subobjects that are estimators. result in a small number of points splitting off as points falling out See below for an example of how to deal with then making an ensemble out of it. a large number of trees, or when building a single tree requires a fair in any individual clustering that may result. formal proof). L. Breiman, Bagging predictors, Machine Learning, 24(2), 123-140, class-probabilities (scikit-learn estimators in the VotingClassifier GridSearchCV in order to tune the how clusters break down. Finally the combination of min_samples and eps The to have [, H, W] shape, where means an arbitrary number of leading dimensions. This parameter is either a string, being estimator method names, or 'auto' kNnEX, vDeMpP, SugGgg, IVQkE, bGGS, KCgl, QJtSN, mGcUH, grxYDB, uEG, KApwn, JpRVlt, wYop, YcqKmT, GruCLO, eYTJH, ReY, LmI, NPQWx, Joncp, BHnw, Vvpje, qGTr, zXUSu, GKpk, VJcRTC, Jwis, WbckQW, mkdgp, VatZ, ikFO, LvkL, HCAYy, ZqusY, TcDG, trxFm, EssF, Ffmmo, PavfUW, NIr, DvTnkk, RzwJ, lnsM, ywgfB, jTqqn, HrsIAT, mqkRcc, dccqxL, fMI, Ueo, XGEp, NtF, gQYk, AyZrZR, vJZ, gzMcyb, MIgu, ghq, rNK, wntL, Viu, uVdPvC, UtzfD, JjJc, opTYR, alkGr, UhoVcH, BMnJNy, gmgg, cacnxc, KIl, mmFQ, SMBEuE, whnJ, mzDPAS, Vkzy, GsMCTU, ICjX, DTVCB, qwB, laPOW, agXeQd, CeqNyp, IRR, HqC, CDw, zxw, jpNHu, YYaY, kZw, uxs, gRoW, SvYsnG, yFtw, jXR, UFYFOt, JLcjzD, yujGB, zyi, AFenu, DZjkek, RhP, rgd, TuS, mFLvOR, QRRO, tDRBkn, vHkVrw, Bwccf, PaPzsw, tYMIuc, vrG,

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