FX - Predictive analysis @ AI and Algorithm @ MentDB Weak
Predictive analysis
pa polynomial_curve_fit_get_coeffpa polynomial_curve_fit_evalpa polynomial_curve_fit_eval_incrpa xy_scatterpa xy_scatterpa rl loadpa rl load_from_jsonpa rl load_emptypa rl existpa rl showpa rl add_datapa rl slopepa rl interceptpa rl predictpa rl intercept_std_errpa rl mean_square_errorpa rl countpa rl rpa rl sum_squarespa rl r_squarepa rl significancepa rl slope_confidence_intervalpa rl slope_std_errpa rl sum_squared_errorspa rl total_sum_squarespa rl x_sum_squarespa rl slope_confidence_intervalpa rl closepa rl close_allpa rm loadpa rm load_from_jsonpa rm set_no_interceptpa rm calculate_adjusted_r_squaredpa rm calculate_residual_sum_of_squarespa rm calculate_r_squaredpa rm calculate_total_sum_of_squarespa rm estimate_error_variancepa rm estimate_regressand_variancepa rm estimate_regression_standard_errorpa rm estimate_regression_parameters_variancepa rm estimate_residualspa rm estimate_regression_parameters_standard_errorspa rm estimate_regression_parameterspa rm predictpa rm existpa rm showpa rm closepa rm close_all
pa polynomial_curve_fit_get_coeff <data> <degree>
Description
Train and get polynomial curve coeff from [x,y]
Train and get polynomial curve coeff from [x,y]
Parameters
data: The json array of x,y - string - required
degree: The degree of the polynomial curve - number - required
data: The json array of x,y - string - required
degree: The degree of the polynomial curve - number - required
admin
pa polynomial_curve_fit_get_coeff "[ [1.0, 2.0], [1.0, 2.2], [1.0, 2.5], [1.2, 2.0], [1.11, 0.9], [5.0, 3.0], [5.2, 3.08], [5.1, 3.0], [5.15, 2.9] ]" "3"
mentdb
"[7.499085083330208,-7.6240029798174,2.4585392371279067,-0.22286556119637468]"
pa polynomial_curve_fit_eval <coeff> <x>
Description
Eval a polynomial curve for specific value
Eval a polynomial curve for specific value
Parameters
coeff: The json array coeff - string - required
x: The x to eval - number - required
coeff: The json array coeff - string - required
x: The x to eval - number - required
admin
pa polynomial_curve_fit_eval "[7.499085083330208,-7.6240029798174,2.4585392371279067,-0.22286556119637468]" "2"
mentdb
0.30231158263603675
pa polynomial_curve_fit_eval_incr <coeff> <min_x> <max_x> <increment>
Description
Eval a polynomial curve for a range
Eval a polynomial curve for a range
Parameters
coeff: The json array coeff - string - required
min_x: The min x to eval - number - required
max_x: The max x to eval - number - required
increment: The increment - number - required
coeff: The json array coeff - string - required
min_x: The min x to eval - number - required
max_x: The max x to eval - number - required
increment: The increment - number - required
admin
pa polynomial_curve_fit_eval_incr "[7.499085083330208,-7.6240029798174,2.4585392371279067,-0.22286556119637468]" 1 5 0.1
mentdb
[[1.0,2.1107557794443395],[1.1,1.7908802205034604],[1.2000000000000002,1.5054663192661772],[1.3000000000000003,1.2531768823653149],[1.4000000000000004,1.032674716433693],[1.5000000000000004,0.8426226281041327],[1.6000000000000005,0.6816834240094574],[1.7000000000000006,0.5485199107824883],[1.8000000000000007,0.44179489505604863],[1.9000000000000008,0.36017118346295796],[2.000000000000001,0.30231158263603675],[2.100000000000001,0.2668788992081117],[2.200000000000001,0.25253593981199796],[2.300000000000001,0.2579455110805249],[2.4000000000000012,0.2817704196465085],[2.5000000000000013,0.322673472142772],[2.6000000000000014,0.37931747520213843],[2.7000000000000015,0.45036523545742746],[2.8000000000000016,0.5344795595414631],[2.9000000000000017,0.6303232540870649],[3.0000000000000018,0.7365591257270543],[3.100000000000002,0.8518499810942535],[3.200000000000002,0.97485862682149],[3.300000000000002,1.1042478695415756],[3.400000000000002,1.2386805158873448],[3.500000000000002,1.3768193724916014],[3.6000000000000023,1.5173272459871887],[3.7000000000000024,1.658866943006906],[3.8000000000000025,1.8001012701835943],[3.9000000000000026,1.9396930341500633],[4.000000000000003,2.0763050415391433],[4.100000000000002,2.2086000989836396],[4.200000000000002,2.335241013116396],[4.300000000000002,2.4548905905702254],[4.400000000000001,2.5662116379779425],[4.500000000000001,2.6678669619723765],[4.6000000000000005,2.7585193691863505],[4.7,2.836831666252677],[4.8,2.9014666598041945],[4.8999999999999995,2.9510871564737036],[4.999999999999999,2.984355962894039]]
pa xy_scatter <jsonArray> <title>
Description
Show a scatter
Show a scatter
Parameters
jsonArray: The json array of x,y - string - required
title: The title - string - required
jsonArray: The json array of x,y - string - required
title: The title - string - required
admin
pa xy_scatter "[ [1.0, 2.0], [1.0, 2.2], [1.0, 2.5], [1.2, 2.0], [1.11, 0.9], [5.0, 3.0], [5.2, 3.08], [5.1, 3.0], [5.15, 2.9] ]" "x, y"
mentdb
In editor ...
pa xy_scatter <cmId> <fieldX> <fieldY> <sqlSource>
Description
Show a predictive analysis scatter
Show a predictive analysis scatter
Parameters
cmId: The database connection id - string - required
fieldX: The field X - string - required
fieldY: The field X - string - required
sqlSource: The select query (origin) - string - required
cmId: The database connection id - string - required
fieldX: The field X - string - required
fieldY: The field X - string - required
sqlSource: The select query (origin) - string - required
admin
pa xy_scatter "demo_cm_mysql" "id" "quantity" "select * from products limit 0, 500"
mentdb
In editor ...
pa rl load <regId> <cmId> <fieldX> <fieldY> <sqlSource>
Description
Load a linear regression from the database
Load a linear regression from the database
Parameters
regId: The regression id - string - required
cmId: The database connection id - string - required
fieldX: The field X - string - required
fieldY: The field X - string - required
sqlSource: The select query (origin) - string - required
regId: The regression id - string - required
cmId: The database connection id - string - required
fieldX: The field X - string - required
fieldY: The field X - string - required
sqlSource: The select query (origin) - string - required
admin
pa rl load "reg1" "demo_cm_mysql" "id" "quantity" "select * from products limit 0, 500";
mentdb
1
pa rl load_from_json <regId> <jsonArray>
Description
Load a linear regression from a JSON array of doubles
Load a linear regression from a JSON array of doubles
Parameters
regId: The regression id - string - required
jsonArray: The json array - string - required
regId: The regression id - string - required
jsonArray: The json array - string - required
admin
pa rl load_from_json "reg1" "[ [1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0], [5.0, 6.0] ]";
mentdb
1
pa rl load_empty <regId>
Description
Load an empty linear regression
Load an empty linear regression
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl load_empty "reg1";
mentdb
1
pa rl exist <regId>
Description
Check if a regression already exist
Check if a regression already exist
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl exist "reg1"
mentdb
1
pa rl show
Description
Show all regressions
Show all regressions
admin
pa rl show
mentdb
[ "reg1" ]
pa rl add_data <regId> <x> <y>
Description
Add data to a regression
Add data to a regression
Parameters
regId: The regression id - string - required
x: The x - number - required
y: The y - number - required
regId: The regression id - string - required
x: The x - number - required
y: The y - number - required
admin
pa rl add_data "reg1" 5 56;
mentdb
1
pa rl slope <regId>
Description
Get the slope (y = intercept + slope * x)
Get the slope (y = intercept + slope * x)
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl slope "reg1"
mentdb
7.25
pa rl intercept <regId>
Description
Get the intercept (y = intercept + slope * x)
Get the intercept (y = intercept + slope * x)
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl intercept "reg1"
mentdb
-11.5
pa rl predict <regId> <x>
Description
Make a prediction
Make a prediction
Parameters
regId: The regression id - string - required
x: The x - number - required
regId: The regression id - string - required
x: The x - number - required
admin
pa rl predict "reg1" 12
mentdb
75.5
pa rl intercept_std_err <regId>
Description
Returns the standard error of the intercept estimate, usually denoted s(b0).
Returns the standard error of the intercept estimate, usually denoted s(b0).
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl intercept_std_err "reg1"
mentdb
19.764235376052373
pa rl mean_square_error <regId>
Description
Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.
Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl mean_square_error "reg1"
mentdb
390.625
pa rl count <regId>
Description
Get the number of couple
Get the number of couple
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl count "reg1"
mentdb
6
pa rl r <regId>
Description
Returns Pearson's product moment correlation coefficient, usually denoted r.
Returns Pearson's product moment correlation coefficient, usually denoted r.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl r "reg1"
mentdb
0.5564589284286688
pa rl sum_squares <regId>
Description
Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).
Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl sum_squares "reg1"
mentdb
700.8333333333334
pa rl r_square <regId>
Description
Returns the coefficient of determination, usually denoted r-square.
Returns the coefficient of determination, usually denoted r-square.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl r_square "reg1"
mentdb
0.3096465390279824
pa rl significance <regId>
Description
Returns the significance level of the slope (equiv) correlation.
Returns the significance level of the slope (equiv) correlation.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl significance "reg1"
mentdb
0.2514643980065754
pa rl slope_confidence_interval <regId>
Description
Returns the half-width of a 95% confidence interval for the slope estimate.
Returns the half-width of a 95% confidence interval for the slope estimate.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl slope_confidence_interval "reg1"
mentdb
15.027949957243381
pa rl slope_std_err <regId>
Description
Returns the standard error of the slope estimate, usually denoted s(b1).
Returns the standard error of the slope estimate, usually denoted s(b1).
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl slope_std_err "reg1"
mentdb
5.412658773652741
pa rl sum_squared_errors <regId>
Description
Returns the sum of squared errors (SSE) associated with the regression model.
Returns the sum of squared errors (SSE) associated with the regression model.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl sum_squared_errors "reg1"
mentdb
1562.5
pa rl total_sum_squares <regId>
Description
Returns the sum of squared deviations of the y values about their mean.
Returns the sum of squared deviations of the y values about their mean.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl total_sum_squares "reg1"
mentdb
2263.3333333333335
pa rl x_sum_squares <regId>
Description
Returns the sum of squared deviations of the x values about their mean.
Returns the sum of squared deviations of the x values about their mean.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl x_sum_squares "reg1"
mentdb
13.333333333333334
pa rl slope_confidence_interval <regId> <alpha>
Description
Returns the half-width of a (100-100*alpha)% confidence interval for the slope estimate.
Returns the half-width of a (100-100*alpha)% confidence interval for the slope estimate.
Parameters
regId: The regression id - string - required
alpha: The double alpha - number - not required
regId: The regression id - string - required
alpha: The double alpha - number - not required
admin
pa rl slope_confidence_interval "reg1" 0.2
mentdb
51.80063969449396
pa rl close <regId>
Description
Close a regression
Close a regression
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rl close "reg1";
mentdb
1
pa rl close_all
Description
Close all regressions
Close all regressions
admin
pa rl close_all;
mentdb
1
pa rm load <regId> <cmId> <fieldX1> <fieldX2> <fieldX3> <fieldX4> <fieldX5> <fieldY> <sqlSource>
Description
Load a multiple regression from the database
Load a multiple regression from the database
Parameters
regId: The regression id - string - required
cmId: The database connection id - string - required
fieldX1: The field X1 - string - required
fieldX2: The field X2 - string - required
fieldX3: The field X3 - string - required
fieldX4: The field X4 - string - required
fieldX5: The field X5 - string - required
fieldY: The field X - string - required
sqlSource: The select query (origin) - string - required
regId: The regression id - string - required
cmId: The database connection id - string - required
fieldX1: The field X1 - string - required
fieldX2: The field X2 - string - required
fieldX3: The field X3 - string - required
fieldX4: The field X4 - string - required
fieldX5: The field X5 - string - required
fieldY: The field X - string - required
sqlSource: The select query (origin) - string - required
admin
pa rm load "reg1" "demo_cm_mysql" "id" "quantity" "" "" "" "price" "select * from products limit 0, 500";
mentdb
1
pa rm load_from_json <regId> <jsonArrayX> <jsonArrayY>
Description
Load a multilple regression from two JSON array of doubles
Load a multilple regression from two JSON array of doubles
Parameters
regId: The regression id - string - required
jsonArrayX: The json array X - string - required
jsonArrayY: The json array Y - string - required
regId: The regression id - string - required
jsonArrayX: The json array X - string - required
jsonArrayY: The json array Y - string - required
admin
pa rm load_from_json "reg1" "[ [ 1.0, 23.457 ], [ 2.0, 29.987 ], [ 3.0, 89.987 ], [ 4.0, 99.098 ], [ 5.0, 123.08 ] ]" "[7.5, 9.8, 14.7, 14.7, 19.4]";
mentdb
1
pa rm set_no_intercept <regId> <bool>
Description
Set no intercept
Set no intercept
Parameters
regId: The regression id - string - required
bool: The boolean - boolean - required
regId: The regression id - string - required
bool: The boolean - boolean - required
admin
pa rm set_no_intercept "reg1" true
mentdb
1
pa rm calculate_adjusted_r_squared <regId>
Description
Returns the adjusted R-squared statistic, defined by the formula R2adj = 1 - [SSR (n - 1)] / [SSTO (n - p)]
Returns the adjusted R-squared statistic, defined by the formula R2adj = 1 - [SSR (n - 1)] / [SSTO (n - p)]
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm calculate_adjusted_r_squared "reg1"
mentdb
0.9930302201822587
pa rm calculate_residual_sum_of_squares <regId>
Description
Returns the sum of squared residuals.
Returns the sum of squared residuals.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm calculate_residual_sum_of_squares "reg1"
mentdb
2.6787094169120644
pa rm calculate_r_squared <regId>
Description
Returns the R-Squared statistic, defined by the formula R2 = 1 - SSR / SSTO
Returns the R-Squared statistic, defined by the formula R2 = 1 - SSR / SSTO
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm calculate_r_squared "reg1"
mentdb
0.9972120880729035
pa rm calculate_total_sum_of_squares <regId>
Description
Returns the sum of squared deviations of Y from its mean.
Returns the sum of squared deviations of Y from its mean.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm calculate_total_sum_of_squares "reg1"
mentdb
960.8299999999999
pa rm estimate_error_variance <regId>
Description
Estimates the variance of the error.
Estimates the variance of the error.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm estimate_error_variance "reg1"
mentdb
1.3393547084560322
pa rm estimate_regressand_variance <regId>
Description
Returns the variance of the regressand, ie Var(y).
Returns the variance of the regressand, ie Var(y).
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm estimate_regressand_variance "reg1"
mentdb
21.746999999999996
pa rm estimate_regression_standard_error <regId>
Description
Estimates the standard error of the regression.
Estimates the standard error of the regression.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm estimate_regression_standard_error "reg1"
mentdb
1.1573049332202954
pa rm estimate_regression_parameters_variance <regId>
Description
Estimates the variance of the regression parameters, ie Var(b).
Estimates the variance of the regression parameters, ie Var(b).
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm estimate_regression_parameters_variance "reg1"
mentdb
[ [ 1.1939297361248733, -0.6413091167219049, 0.012718472658507332 ], [ -0.6413091167219049, 1.3402027085716288, -0.04621465840546843 ], [ 0.012718472658507332, -0.04621465840546843, 0.0017221335163781243 ] ]
pa rm estimate_residuals <regId>
Description
Estimates the residuals, ie u = y - X*b.
Estimates the residuals, ie u = y - X*b.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm estimate_residuals "reg1"
mentdb
[ -0.21160225532182864, 0.39217249531173515, 0.5051039893042244, -1.3403164432563521, 0.6546422139622123 ]
pa rm estimate_regression_parameters_standard_errors <regId>
Description
Returns the standard errors of the regression parameters.
Returns the standard errors of the regression parameters.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm estimate_regression_parameters_standard_errors "reg1"
mentdb
[ 1.264553444360703, 1.3397786414221338, 0.04802653051960952 ]
pa rm estimate_regression_parameters <regId>
Description
Estimates the regression parameters b.
Estimates the regression parameters b.
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm estimate_regression_parameters "reg1"
mentdb
[ 5.036908634809639, 1.3187573895222953, 0.05780518527475357 ]
pa rm predict <regId> <jsonX>
Description
Make a prediction
Make a prediction
Parameters
regId: The regression id - string - required
jsonX: The json that contains an array of x - string - required
regId: The regression id - string - required
jsonX: The json that contains an array of x - string - required
admin
pa rm predict "reg1" "[12, 34]"
mentdb
22.827373608418803
pa rm exist <regId>
Description
Check if a regression already exist
Check if a regression already exist
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm exist "reg1"
mentdb
1
pa rm show
Description
Show all regressions
Show all regressions
admin
pa rm show
mentdb
[ "reg1" ]
pa rm close <regId>
Description
Close a regression
Close a regression
Parameters
regId: The regression id - string - required
regId: The regression id - string - required
admin
pa rm close "reg1";
mentdb
1
pa rm close_all
Description
Close all regressions
Close all regressions
admin
pa rm close_all;
mentdb
1