Ąi ddlZGddZy)Nc.eZdZdZddZdZddZd dZy) StabilityAnalyzera  Measures prediction stability across perturbations. Core principle: What doesn't break is an emergent truth. If a classifier gives consistent predictions on perturbed versions of the same input, that prediction is "stable" and likely correct. c.||_||_||_y)z Args: classifier: MLPClassifier instance with predict() and forward() n_perturbations: Number of perturbed samples per input noise_level: Standard deviation of Gaussian noise N) classifiern_perturbations noise_level)selfrrrs ?/home/per/Documents/python/state to state/stability_analyzer.py__init__zStabilityAnalyzer.__init__ s%.&c*|jd}tj||jft}tj||jdf}t |jD]~}|tj j|j|jzz}|jj|}|jj||dd|f<||dd|f<tj|t}tj|} t |D]L} tj|| d} tj| || <| || |jz | | <Ntj|d} | || fS)aW Measure how stable predictions are for input X under noise. Returns: stability_scores: Array [batch_size] - fraction of consistent predictions predicted_labels: Array [batch_size] - most common prediction confidence: Array [batch_size, n_classes] - prediction distribution r)dtype N) minlength)axis)shapenpzerosrintrangerandomrandnrrforwardpredictbincountargmaxmean) r X batch_sizeall_predictionsall_probabilitiesi X_perturbedprobsmode_predictionsstability_scoresjcounts confidences r measure_stabilityz#StabilityAnalyzer.measure_stabilityskWWQZ ((J0D0D#ESQHHj$2F2F%KLt++, ,Abiiooqww7$:J:JJJKOO++K8E$(OO$;$;K$HOAqD !&+ ad #  ,88Jc:88J/z" UA[[!3rBF"$))F"3 Q "()9!)<"=@T@T"T Q  U WW.Q7 !1:==r c^|j|\}}}||k\}|||||||||dS)z Find samples where predictions are highly stable. Returns: stable_mask: Boolean mask of stable samples stable_X: Stable input samples stable_labels: Their emergent truth labels )maskrlabels stabilityr*)r+)r r thresholdr' predictionsr* stable_masks r find_stable_samplesz%StabilityAnalyzer.find_stable_samples9sT594J4J14M1+z&)3  ;!+.)+6$[1   r c6|jddkryg}ttt|dz dD]}||||dz}}t j dd|ddtj f}|tj ddfd|z z|tj ddf|zz}|jj|} t j| dd| ddk7} d| |dz z z } |j| |rt j|SdS) z Analyze decision boundary stability by interpolating between samples. Returns: boundary_stability: How stable predictions are near boundaries rgrrNg?) rrminlenrlinspacenewaxisrrsumappendr) r rn_steps stabilitiesr#x1x2 interpolationX_interppreds transitionsr/s r analyze_decision_boundariesz-StabilityAnalyzer.analyze_decision_boundariesMs 771:> s3q6A:r*+ *AqT1QU8BKK1g6q"**}EM"**a-(A ,=>BJJPQMARUbAbbHOO++H5E&&suQRy!89K{gk:;I   y ) *(3rww{#;;r N)g?)g?)2)__name__ __module__ __qualname____doc__r r+r3rEr r rrs '>B (rNsa<a