Problems

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Streaming Min-Max Normalization

Feature EngineeringData Processing

Medium

Implement streaming min-max normalization: given multiple batches of data, update per-feature running min and max, and normalize each incoming batch.

Streaming normalization processes data in real-time without knowing the full dataset statistics upfront. Your implementation should maintain running minimum and maximum values for each feature, updating them as new batches arrive, then normalize each batch using the current global statistics.

Normalization Formula:

x' = \frac{x - \min}{\max - \min + \epsilon}

Function Arguments

D: int - Number of features
state: dict - Contains 'min' and 'max' arrays (shape D,)
X_batch: array-like, shape (B, D) - Input batch
eps: float - Small value to avoid division by zero

Loading visualization...

Examples

Input: streaming_minmax_init(D=2), streaming_minmax_update(state, [[1,3],[2,1]])

Output: streaming_minmax_init({'min': [inf,inf], 'max': [-inf,-inf]}), streaming_minmax_update([[0,1],[1,0]])

Input: streaming_minmax_init(D=1), streaming_minmax_update(state, [[5],[3]])

Output: streaming_minmax_init({'min': [inf], 'max': [-inf]}), streaming_minmax_update([[1],[0]])

Hint 1

Initialize with np.full() for min and max arrays.

Hint 2

Use np.minimum() and np.maximum() to update running statistics.

Hint 3

Use np.maximum() to handle constant features safely with eps.

Requirements

On init: min=+inf, max=-inf
On update: state['min']=min(state['min'], batch_min); same for max
Normalize using the updated min/max (post-update) for consistency
Handle constant columns (range≈0) via eps
Pure NumPy implementation

Constraints

Handles constant columns (range≈0) via eps
Batches can arrive in any order/size
NumPy only; time limit: 300ms

Try Similar Problems

Minmax Normalization One Hot Encoding Min Max Scaling Zscore Standardization Robust Scaling

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Streaming Min-Max Normalization

Feature EngineeringData Processing

Medium

Implement streaming min-max normalization: given multiple batches of data, update per-feature running min and max, and normalize each incoming batch.

Normalization Formula:

x' = \frac{x - \min}{\max - \min + \epsilon}

Function Arguments

D: int - Number of features
state: dict - Contains 'min' and 'max' arrays (shape D,)
X_batch: array-like, shape (B, D) - Input batch
eps: float - Small value to avoid division by zero

Loading visualization...

Examples

Input: streaming_minmax_init(D=2), streaming_minmax_update(state, [[1,3],[2,1]])

Output: streaming_minmax_init({'min': [inf,inf], 'max': [-inf,-inf]}), streaming_minmax_update([[0,1],[1,0]])

Input: streaming_minmax_init(D=1), streaming_minmax_update(state, [[5],[3]])

Output: streaming_minmax_init({'min': [inf], 'max': [-inf]}), streaming_minmax_update([[1],[0]])

Hint 1

Initialize with np.full() for min and max arrays.

Hint 2

Use np.minimum() and np.maximum() to update running statistics.

Hint 3

Use np.maximum() to handle constant features safely with eps.

Requirements

On init: min=+inf, max=-inf
On update: state['min']=min(state['min'], batch_min); same for max
Normalize using the updated min/max (post-update) for consistency
Handle constant columns (range≈0) via eps
Pure NumPy implementation

Constraints

Handles constant columns (range≈0) via eps
Batches can arrive in any order/size
NumPy only; time limit: 300ms

Try Similar Problems

Minmax Normalization One Hot Encoding Min Max Scaling Zscore Standardization Robust Scaling

Case 1

Case 2

D =

Accepts: number

batches =

Accepts: array

Implement streaming min-max normalization: given multiple batches of data, update per-feature running min and max, and normalize each incoming batch.

Normalization Formula:

x' = \frac{x - \min}{\max - \min + \epsilon}

Function Arguments

D: int - Number of features
state: dict - Contains 'min' and 'max' arrays (shape D,)
X_batch: array-like, shape (B, D) - Input batch
eps: float - Small value to avoid division by zero