`Name:`

Tot_info `Domain:`

Algorithm`Functionality:`

Printing the Kullbacks information measure, the degree of freedom and the possibility density of distribution of one table.`Input:`

T: N tables(Type: Table)

`Output:`

For each table tiT, $I_i$: The Kullbacks information measure(Type: ?) $D_i$: The degree of freedom(Type: Integer) $Q_i$: the possibility density of distribution of one table(Type: ?)

`Source input:`

$T_s$ ;
`Source output:`

$I^s_i,D^s_i,Q^s_i$ `Follow-up input:`

$T_f$ ;
`Follow-up output:`

$I^f_i,D^f_i,Q^f_i$ `Input relation:`

$T_f = T_s \cup T_s$`Output relation:`

$(tot_I)_f$ = $2 \times (tot_I)_s$, where $tot_I$ is the summaries of all $I_i$.
$(tot_D)_f$ = $2 \times (tot_D)_s$, where $tot_D$ is the summaries of all $D_i$.
$Q_f = Q_s$
`Source input:`

$T_s$ ;
`Source output:`

$I^s_i,D^s_i,Q^s_i$ `Follow-up input:`

$T_f$ ;
`Follow-up output:`

$I^f_i,D^f_i,Q^f_i$ `Input relation:`

$R^f_i = 2 \times R^s_i$
$C^f_i = C^s_i$
where $R_i$ is the number of rows of $t_i$, $C_i$ is the number of columns of $t_i$ ,and the content from the ($R^s_i+1$)th row to the ($2 \times R^s_i$)th row in $t^f_i$ is the duplicate of its first $r^s_i$ rows.
`Output relation:`

$I^f_i = 2 \times I^s_i$
$D^f_i = D^s_i + R^s_i \times (C^s_i - 1)$
$Q_f = Q_s$
`Source input:`

$T_s$ ;
`Source output:`

$I^s_i,D^s_i,Q^s_i$ `Follow-up input:`

$T_f$ ;
`Follow-up output:`

$I^f_i,D^f_i,Q^f_i$ `Input relation:`

$T_f$ = Each element of $T_s$ multiplied by k.
`Output relation:`

$I^f_i = k \times I^s_i$
$D^f_i = D^s_i$
$Q_f = Q_s$