Familiarise yourself with affinity propagation, for example by working through
https://en.wikipedia.org/wiki/Affinity_propagation.
Task 1: Prepare a test dataset
Pick a small set of values that enable you to apply clustering using affinity propagation on paper.
Marking information: Up to 10 points: clarify why you believe your small dataset to be suitable for clustering.
Task 2: Apply affinity propagation on paper
Work through your small dataset from Task 1 to ensure you have known-good results.
Marking information: Up to 20 points: include information on how you sanity-checked your results as the number of iterations may considerably affect the precision of your final values.
Task 3: Implement affinity propagation in Python
You are now set to actually add the implementation. Note that you are expected to fully implement the mathematical operations instead of using a library function such as scikit or statsmodels.
Your implementation may make several assumptions about the inputs provided to it. Make those explicit in comments. Also, provide an estimate on how long execution of your algorithm will take dependent on the input values. Express this estimate as a function of the inputs, such as the number of clusters and/or the number of data points.
Marking information: Up to 50 points: 30 points for a correctly working Python implementation, 10 points for describing limitations and assumptions of your implementation, and 10 points for a description of the complexity of your algorithm.
Task 4: Create a test harness
Test your implementation via unit and integration tests. As part of this work, you may choose to compare to a reference implementation, such as sci-kit learn’s.
Marking information: Up to 20 points: identify suitable unit tests and integration tests.
Explain what coverage you expect your test suite to have.
Affinity propagation (AP), a clustering technique used in statistics and data mining, is based on the idea of “message passing” between data points. [1] Affinity propagation does not require the number of clusters to be known or estimated prior to starting the method, unlike clustering algorithms like k-means or k-medoids. Similar to k-medoids, affinity propagation identifies “exemplars,” or input set participants, who are typical of clusters. [1] Without making any assumptions about the underlying structure of the data points, define x1 through xn, and define s as a function that measures the similarity between any two points, such that s(i, j) > s(i, k) iff xi is more similar.
outcome in reasonable nursing programs and to recognize scholastically lacking understudies. Level of instructive fulfillment as estimated by end of (GED) program, secondary everyday schedule courses were not viewed as a helpful indicator of progress in LPN programs. The capacity of orientation to anticipate achievement was restricted. Albeit the review viewed age as connected with section or disappointment of the LPN Exit Competency Test, generally speaking age was not a decent indicator of outcome in LPN programs. Race likewise was not an indicator of progress. Trial of Adult Basic Education (TABE) perusing scores represented the biggest level of change in every one of the measure factors in both single and various relapse examination. The R was 0.0740 for the fluctuation in section or disappointment of the LPN Assessment Test (AT) represented by TABE understanding scores. TABE science scores represented just a little percent of the fluctuation (7T = 0.0151). Quick’s (1989) investigation of 1,254 useful nursing understudies in 12 postsecondary schools in Georgia exhibited no huge distinction between paces of program culmination for either secondary school graduates or GED beneficiaries. Nonetheless, GED beneficiaries had fundamentally higher combined grade point midpoints when contrasted with secondary school graduates. Though age displayed no huge relationship to the presentation as far as GPA and NCLEX results, race was viewed as a calculate that African American understudies would in general have lower section rates in contrast with the exhibition of Caucasian understudies on the NCLEX-PN. Achievement is prevalently characterized in the functional nursing research writing as finishing on the NCLEX-PN assessment. Thompson (1989) viewed as SAT verbal, race, and progress in years to be indicators of achievement for the down to earth nursing licensure assessment. A portion of these discoveries were disconnected to results revealed in later examinations. The example comprised of 248 pragmatic nursing understudies across the province of Georgia who graduated in 1987 and composed the authorizing assessment. Autonomous factors included Test of Adult Basic Education scores; three sub-test scores of the Career Planning Program Test including perusing, mathematical abilities, language abilities, SAT, ACT, secondary school grade point normal, age, and race. Sub-scores of every confirmation test were utilized as indicator factors in stepwise numerous relapse examinations and investigation of fluctuation was utilized to dissect the impact old enough on licensure achievement or disappointment. The mean age for the subjects who bombed the nursing assessment was 24.55, while the mean age for the subjects who breezed through the assessment was 27.82. Caucasians were bound to have passing scores when contrasted with minorities, and the specialist likewise found a huge, positive connection between SAT verbal scores and nursing licensure scores as well as between Career Program Reading Test scores and nursing licensure scores. Chi-square examination tracked down rural projects to have fundamentally better section rates when contrasted with the entry paces of projects situated in metropolitan or rustic regions. Chosen factors that could influence whittling down in pragmatic nursing projects and accomplishment on the NCLEX-PN in Alabama were analyzed. Factors found to have a relationship to program result were age and race. The most youthful age gatherings (17 – 24) were found to have a lower achievement rate in LPN programs (Parrish, 1994).
The Development of Nurses in Missouri Nursing advancement and examination in Missouri has been significant as the more seasoned grown-up populace rate kept on expanding in Missouri proportionate to the remainder of the state populace. The nursing schools in Missouri started to address wellbeing necessities and worries of more seasoned grown-ups from an imaginative and cooperative point of view which incorporated the college PC designing and wellbeing informatics programs (Beeman, 2001). The Aging in Place project was imagined by the school of nursing staff in 1996. It created to include Senior Care, a nursing practice zeroed in on health; and TigerPlace, a private climate of imaginative plan where pets were invited and that elaborate the school of veterinary medication. Among the subjects talked about were all encompassing methodologies, diabetes, diseases, and advances across care settings, drug treatment, fall avoidance, constant psychological instability, and mental disability. Proceeding with training units were granted to medical attendants, nursing home executives, and social laborers who went to the gathering (Lockie, 2000). Confirmations Criteria
Confirmations models utilized for passage into nursing programs shift among programs. In an assessment of junior colleges confirmations rules, researchers have found an extensive variety of required pre-essential affirmations courses, units, and different measures among programs (Ali, 2008). The writing uncovered the consideration of different scholastic affirmations standards, including GPA, SAT, ACT, NLN, Preadmissions Test, Nurse Entrance Test (NET), and the Written Proficiency Examination (WPE), TEAS. Late worries in regards to grade expansion, and the prescient worth of GPA, has brought about the more regular consideration of state administered tests as a component of the confirmations models in recognizing understudies who are probably