A package to consume the Atlassian Stride API
Stride API Client
MCP server wrapping the Open Bus Stride API by Hasadna - Israeli public transit data
Easily create Atlassian Documents for use with the Stride API
Normalize array (possibly n-dimensional) to zero mean and unit variance
Return a normal number `y` and exponent `exp` satisfying `x = y * 2^exp`.
Return the stride along a specified dimension for a provided ndarray.
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Determine the index offset which specifies the location of the first indexed value in a strided array.
TypeScript definitions for ndarray
Find [nd-]array min/max values
Multiply a vector by a scalar constant.
Return the minimum accessible index based on a set of provided strided array parameters.
A client library for interacting with the Open-Bus API.
Multiply a double-precision floating-point vector by a constant.
Generate a stride array from an array shape.
Determine the order of a multidimensional array based on a provided stride array.
Calculate the median value of a sorted strided array.
Convert a strided array and associated metadata to an object likely to have the same "shape".
Convert a strided array and associated metadata to an object likely to have the same "shape".
Wrapper for Microsoft's C JPEG XR image codec library
Given a stride array, determine whether an array is row-major.
Calculate the range of a strided array.
Converts between common geometry position formats.
Ruby wrapper for the Stride API
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.
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