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weather-databot.madis> (pp/print-table [:name :shape] (vals mesonet-overview))

|                   :name |       :shape |
|-------------------------+--------------|
|             handbook5Id |   [138878 6] |
|          precipAccumQCD |  [138878 10] |
|              dewpointDD |     [138878] |
|              lastRecord |      [45000] |
|        roadTemperature2 |     [138878] |

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reader-composition  #(-> source-image
                                  (dtt/select :all :all [2 1 0])
                                  (dfn/+ 50)
                                  ;;Clamp top end to 0-255
                                  (dfn/min 255)
                                  (dtype/copy! (dtype/from-prototype source-image)))

         inline-fn #(as-> source-image dest-image
                      (dtt/select dest-image :all :all [2 1 0])

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(ns data.model.key-value
  (:require [datomic.api :as d]
            [clojure.edn :as edn])
  (:import [java.util UUID]))


(def datatypes
  {:string {:write str
            :read identity}
   :keyword {:write name

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  :top-models ({:error 0.013333333333333345,
                :name :leukemia,
                :options {:model-type :classification, :system-name :libsvm},
                :system :libsvm,
                :type :classification}
               {:error 0.013333333333333345,
                :name :leukemia,
                :options {:model-type :classification, :system-name :libsvm},
                :system :libsvm,
                :type :classification}

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(defn gridsearch-the-things
  []
  (let [base-systems [{:system-name :xgboost
                       :model-type :classification}
                      {:system-name :smile/classification
                       :model-type :svm}
                      {:system-name :smile/classification
                       :model-type :knn}
                      {:system-name :smile/classification
                       :model-type :ada-boost}]]

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chrisn@chrisn-dt:~/dev/fsof/fsof$ cat scripts/loc.sh


#!/bin/bash

## Arguments are passed through to git log

CMD_ARGS="$@"

git diff $(git log --reverse $CMD_ARGS --pretty=format:"%h" | sed -e 1b -e '$!d') --shortstat

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(->> {:a {:ab {:abc 4
                           :bdd 5}}
                  :b {:bc {:abc 6
                           :bcd 7}}}
                 (nested-map-flatten [:type :name]))
                      
({:abc 4, :bdd 5, :name :ab, :type :a}
 {:abc 6, :bcd 7, :name :bc, :type :b})

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({:fsof.data.model.video-set/face-count 2088414,
  :fsof.data.model.video-set/name "min-face-size-40",
  :fsof.data.model.video-set/uuid #uuid "5b3f93da-f9a4-4431-bbb5-37cea2df6c3c"}
 {:fsof.data.model.video-set/face-count 1287144,
  :fsof.data.model.video-set/name "min-face-size-80",
  :fsof.data.model.video-set/uuid #uuid "5b3f93eb-7cfd-411b-8caa-f903733e76b9"}
 {:fsof.data.model.video-set/face-count 739941,
  :fsof.data.model.video-set/name "min-face-size-160",
  :fsof.data.model.video-set/uuid #uuid "5b3f93fb-ab73-42f2-b673-45f45c213847"})

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Trained:  19 0.00 0.20 0.00  6 1.00 1.00 45  [0.001294] <- new best
Trained:  18 0.00 0.20 0.00  5 1.00 1.00 45  [0.001312] 
Trained:  17 0.10 0.00 0.30  7 1.20 0.70  5  [0.250000] 
Trained:  16 0.30 1.00 0.10  4 0.90 0.70 45  [0.001531] 
Trained:  15 0.00 0.00 0.00  1 0.70 0.70  5  [0.250000] 
Trained:  14 0.00 0.80 0.20  9 0.80 1.00 45  [0.001390] 
Trained:  13 0.00 0.00 0.30  2 0.80 0.90 45  [0.250000] 
Trained:  12 0.30 0.80 0.00  9 0.90 1.00 35  [0.001526] 
Trained:  11 0.20 1.00 0.00  1 1.30 1.00 45  [0.001132] <- new best
Trained:  10 0.10 1.00 0.00  7 0.70 0.90 45  [0.001388] 

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{:num-account-videos 466,
 :num-checkins 563,
 :summaries {:appearance-system {:dlib-correlation {:match-depth {:max 99.0,
                                                                  :mean 17.63100137174211,
                                                                  :median 6,
                                                                  :min 0.0,
                                                                  :variance 559.7853562760971},
                                                    :match-score {:max 1.1787557637974377,
                                                                  :mean 0.867808825869121,
                                                                  :median 0.8816001907900003,