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Posted to commits@systemml.apache.org by de...@apache.org on 2016/01/04 21:09:46 UTC
[1/4] incubator-systemml git commit: change quotation mark from utf-8
to ascii
Repository: incubator-systemml
Updated Branches:
refs/heads/gh-pages 918605fed -> 4193b47c2
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/135a0c0d/algorithms-classification.md
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diff --git a/algorithms-classification.md b/algorithms-classification.md
index 74539fd..aa902ea 100644
--- a/algorithms-classification.md
+++ b/algorithms-classification.md
@@ -64,7 +64,7 @@ Prob[y_i\,{=}\,0\mid x_i; \beta] \,=\,
Here category label 0
serves as the *baseline*, and function $$\exp(\beta_0 + x_i\beta_{1:m})$$
-shows how likely we expect to see “$y_i = 1$” in comparison to the
+shows how likely we expect to see "$y_i = 1$" in comparison to the
baseline. Like in a loaded coin, the predicted odds of seeing 1 versus 0
are $$\exp(\beta_0 + x_i\beta_{1:m})$$ to 1, with each feature $$x_{i,j}$$
multiplying its own factor $\exp(\beta_j x_{i,j})$ to the odds. Given a
@@ -271,7 +271,7 @@ for initial values.
| OBJ\_DROP\_RATIO | Actual-to-predicted reduction ratio, used to update the trust region |
| IS\_POINT\_UPDATED | $1 = {}$new point accepted; $0 = {}$new point rejected, old point restored |
| GRADIENT\_NORM | L2-norm of the loss function gradient (omitted if point is rejected) |
-| RUST\_DELTA | Updated trust region size, the “delta” |
+| RUST\_DELTA | Updated trust region size, the "delta" |
* * *
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/135a0c0d/algorithms-clustering.md
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diff --git a/algorithms-clustering.md b/algorithms-clustering.md
index 19f2a01..023a7f5 100644
--- a/algorithms-clustering.md
+++ b/algorithms-clustering.md
@@ -70,7 +70,7 @@ $c_j := mean(S_j)$
After Step 1, the centroids are generally
different from the cluster means, so we can compute another
-“within-cluster sum of squares” based on the centroids:
+"within-cluster sum of squares" based on the centroids:
$$\textrm{WCSS_C}\,\,=\,\, \sum_{i=1}^n \,\big\|x_i - \mathop{\textrm{centroid}}(S_j: x_i\in S_j)\big\|_2^2
\label{eqn:WCSS:C}$$
@@ -94,7 +94,7 @@ report the best result.
**Scoring.** Our scoring script evaluates the clustering output by comparing it with
a known category assignment. Since cluster labels have no prior
-correspondence to the categories, we cannot count “correct” and “wrong”
+correspondence to the categories, we cannot count "correct" and "wrong"
cluster assignments. Instead, we quantify them in two ways:
1. Count how many same-category and different-category pairs of records end
@@ -104,9 +104,9 @@ each cluster, count the prevalence of its most common category.
The number of categories and the number of clusters ($k$) do not have to
be equal. A same-category pair of records clustered into the same
-cluster is viewed as a “true positive,” a different-category pair
-clustered together is a “false positive,” a same-category pair clustered
-apart is a “false negative” etc.
+cluster is viewed as a "true positive," a different-category pair
+clustered together is a "false positive," a same-category pair clustered
+apart is a "false negative" etc.
### Usage
@@ -210,7 +210,7 @@ centroids)
SystemML Language Reference for details.
**verb**: (default: `0`) `0` = do not print per-iteration statistics for
-each run, `1` = print them (the “verbose” option)
+each run, `1` = print them (the "verbose" option)
### Arguments - K-Means Prediction
@@ -224,7 +224,7 @@ X and C are provided, `prY` is an
output, not input
**spY**: (default: `" "`) Location to read a one-column matrix with the externally
-specified “true” assignment of records (rows) to categories, optional
+specified "true" assignment of records (rows) to categories, optional
for prediction without scoring
**prY**: (default: `" "`) Location to read (or write, if X and
@@ -331,7 +331,7 @@ To predict Y given X and C:
</div>
</div>
-To compare “actual” labels `spY` with “predicted” labels
+To compare "actual" labels `spY` with "predicted" labels
given X and C:
<div class="codetabs">
@@ -357,7 +357,7 @@ given X and C:
</div>
</div>
-To compare “actual” labels `spY` with given “predicted”
+To compare "actual" labels `spY` with given "predicted"
labels prY:
<div class="codetabs">
@@ -601,7 +601,7 @@ well as the number of iterations limit.
* Find the closest centroid for each record, sharing equally any records
with multiple closest centroids.
* Compute the number of records closest to each centroid, checking for
-“runaway” centroids with no records left (in which case the run fails).
+"runaway" centroids with no records left (in which case the run fails).
* Compute the new centroids by averaging the records in their clusters.
When a termination condition is satisfied, we store the centroids and
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/135a0c0d/algorithms-descriptive-statistics.md
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diff --git a/algorithms-descriptive-statistics.md b/algorithms-descriptive-statistics.md
index 3fcdb1a..1ddf01a 100644
--- a/algorithms-descriptive-statistics.md
+++ b/algorithms-descriptive-statistics.md
@@ -183,7 +183,7 @@ be stored. The format of the output matrix is defined by
<a name="table1" />
**Table 1**: The output matrix of `Univar-Stats.dml` has one row per
each univariate statistic and one column per input feature. This table
-lists the meaning of each row. Signs “+” show applicability to scale
+lists the meaning of each row. Signs "+" show applicability to scale
or/and to categorical features.
@@ -267,13 +267,13 @@ feature varies on exponential scale. For example, the mean of {0.01,
0.1, 1.0, 10.0, 100.0} is 22.222, greater than all the sample values
except the largest.
-**Median** (output row 13): The “middle” value that separates the higher half of the
+**Median** (output row 13): The "middle" value that separates the higher half of the
sample values (in a sorted order) from the lower half. To compute the
median, we sort the sample in the increasing order, preserving
duplicates: $v^s_1 \leq v^s_2 \leq \ldots \leq v^s_n$. If $n$ is odd,
the median equals $v^s_i$ where $i = (n\,{+}\,1)\,{/}\,2$, same as the
$50^{\textrm{th}}$ percentile of the sample. If $n$ is even, there are
-two “middle” values $$v^s_{n/2}$$ and $$v^s_{n/2\,+\,1}$$, so we compute the
+two "middle" values $$v^s_{n/2}$$ and $$v^s_{n/2\,+\,1}$$, so we compute the
median as the mean of these two values. (For even $n$ we compute the
$50^{\textrm{th}}$ percentile as $v^s_{n/2}$, not as the median.)
Example: the median of sample {2.2, 3.2, 3.7, 4.4, 5.3, 5.7, 6.1,
@@ -289,11 +289,11 @@ size.
**Interquartile mean** (output row 14): For a sample of a quantitative feature, this is
the mean of the values greater than or equal to the $1^{\textrm{st}}$
quartile and less than or equal the $3^{\textrm{rd}}$ quartile. In other
-words, it is a “truncated mean” where the lowest 25$\%$ and the highest
+words, it is a "truncated mean" where the lowest 25$\%$ and the highest
25$\%$ of the sorted values are omitted in its computation. The two
-“border values”, i.e. the $1^{\textrm{st}}$ and the $3^{\textrm{rd}}$
+"border values", i.e. the $1^{\textrm{st}}$ and the $3^{\textrm{rd}}$
quartiles themselves, contribute to this mean only partially. This
-measure is occasionally used as the “robust” version of the mean that is
+measure is occasionally used as the "robust" version of the mean that is
less sensitive to the extreme values.*
To compute the measure, we sort the sample in the increasing order,
@@ -445,7 +445,7 @@ is far from normal or if the number of samples is small.
**Kurtosis** (output row 10): As a distribution parameter, kurtosis is a measure of the
extent to which feature values cluster around a central point. In other
-words, it quantifies “peakedness” of the distribution: how tall and
+words, it quantifies "peakedness" of the distribution: how tall and
sharp the central peak is relative to a standard bell curve.
Positive kurtosis (*leptokurtic* distribution) indicates that, relative
@@ -705,7 +705,7 @@ statistics for specified pairs of feature columns `X[,i]` and
files with column pairs of interest to the user. Namely, the file given
by `index1` contains the vector of the 1st-attribute column indices and
the file given by `index2` has the vector of the 2nd-attribute column
-indices, with “1st” and “2nd” referring to their places in bivariate
+indices, with "1st" and "2nd" referring to their places in bivariate
statistics. Note that both `index1` and `index2` files should contain a
1-row matrix of positive integers.
@@ -721,7 +721,7 @@ files. The types must be provided as per the following convention:
The script organizes its results into (potentially) four output
matrices, one per each type combination. The types of bivariate
statistics are defined using the types of the columns that were used for
-their arguments, with “ordinal” sometimes retrogressing to “nominal.”
+their arguments, with "ordinal" sometimes retrogressing to "nominal."
[**Table 2**](algorithms-descriptive-statistics.html#table2)
describes what each column in each output matrix
contains. In particular, the script includes the following statistics:
@@ -850,7 +850,7 @@ Pearson’s $\chi^2$ becomes arbitrarily large as
$n\to\infty$ and lands extremely far into the tail of the
$\chi^2$ distribution given a large enough data sample.
$P\textrm{-}$value of Pearson’s $\chi^2$ returns the tail
-“weight” on the right-hand side of Pearson’s $\chi^2$:
+"weight" on the right-hand side of Pearson’s $\chi^2$:
$$P = Prob\big[r \geq \textrm{Pearson’s $\chi^2$} \big|\,\, r \sim \textrm{the $\chi^2$ distribution}\big]$$
@@ -913,11 +913,11 @@ $$\eta^2 \,=\, 1 - \frac{\sum_{i=1}^{n} \big(y_i - \hat{y}[x_i]\big)^2}{\sum_{i=
and $$\bar{y} = (1{/}n)\sum_{i=1}^n y_i$$ is the mean. Value $\hat{y}[x]$
is the average of $y_i$ among all records where $x_i = x$; it can also
-be viewed as the “predictor” of $y$ given $x$. Then
+be viewed as the "predictor" of $y$ given $x$. Then
$$\sum_{i=1}^{n} (y_i - \hat{y}[x_i])^2$$ is the residual error
sum-of-squares and $\sum_{i=1}^{n} (y_i - \bar{y})^2$ is the total
sum-of-squares for $y$. Hence, $\eta^2$ measures the accuracy of
-predicting $y$ with $x$, just like the “R-squared” statistic measures
+predicting $y$ with $x$, just like the "R-squared" statistic measures
the accuracy of linear regression. Our output $\eta$ is the square root
of $\eta^2$.
@@ -937,13 +937,13 @@ frequency counts, the $x_i$-values are treated as fixed (non-random).
To compute $F$ statistic, we first compute $\hat{y}[x]$ as
the average of $y_i$ among all records where $x_i = x$. These
-$\hat{y}[x]$ can be viewed as “predictors” of $y$ given $x$; if $y$ is
-independent on $x$, they should “predict” only the global
+$\hat{y}[x]$ can be viewed as "predictors" of $y$ given $x$; if $y$ is
+independent on $x$, they should "predict" only the global
mean $\bar{y}$. Then we form two sums-of-squares:
- * *Residual* sum-of-squares of the “predictor” accuracy:
+ * *Residual* sum-of-squares of the "predictor" accuracy:
$y_i - \hat{y}[x_i]$.
- * *Explained* sum-of-squares of the “predictor” variability:
+ * *Explained* sum-of-squares of the "predictor" variability:
$\hat{y}[x_i] - \bar{y}$.
$F$ statistic is the ratio of the explained sum-of-squares
@@ -955,9 +955,9 @@ $$F \,\,=\,\,
\frac{n\,{-}\,k}{k\,{-}\,1} \cdot \frac{\eta^2}{1 - \eta^2}$$
Here $k$
-is the domain size of the nominal feature $x$. The $k$ “predictors” lose
+is the domain size of the nominal feature $x$. The $k$ "predictors" lose
1 freedom due to their linear dependence with $\bar{y}$; similarly, the
-$n$ $y_i$-s lose $k$ freedoms due to the “predictors”.
+$n$ $y_i$-s lose $k$ freedoms due to the "predictors".
The statistic can test if the independence hypothesis of $y$ from $x$ is
reasonable; more generally (with relaxed normality assumptions) it can
@@ -986,7 +986,7 @@ coefficient applied to the feature vectors in which all values
are replaced by their ranks, i.e. their positions if the vector is
sorted. The ranks of identical (duplicate) values are replaced with
their average rank. For example, in vector $(15, 11, 26, 15, 8)$ the
-value “15” occurs twice with ranks 3 and 4 per the sorted order
+value "15" occurs twice with ranks 3 and 4 per the sorted order
$(8_1, 11_2, 15_3, 15_4, 26_5)$; so, both values are assigned their
average rank of $3.5 = (3\,{+}\,4)\,{/}\,2$ and the vector is replaced
by $(3.5,\, 2,\, 5,\, 3.5,\, 1)$.
@@ -1071,7 +1071,7 @@ covariate pairs.
Both covariates in each pair must be numerical, with the 2-nd covariate
normally distributed given the 1-st covariate (see Details). Missing
-covariate values or strata are represented by “NaN”. Records with NaN’s
+covariate values or strata are represented by "NaN". Records with NaN’s
are selectively omitted wherever their NaN’s are material to the output
statistic.
@@ -1173,21 +1173,21 @@ the 1-st covariate (i.e. as the feature variable)
**Xcid**: (default: `" "`) Location to read the single-row matrix that lists all index
numbers of the $X$-columns used as the 1-st covariate; the default value
-means “use all $X$-columns”
+means "use all $X$-columns"
**Y**: (default: `" "`) Location to read matrix $Y$ whose columns we want to use as
the 2-nd covariate (i.e. as the response variable); the default value
-means “use $X$ in place of $Y$”
+means "use $X$ in place of $Y$"
**Ycid**: (default: `" "`) Location to read the single-row matrix that lists all index
numbers of the $Y$-columns used as the 2-nd covariate; the default value
-means “use all $Y$-columns”
+means "use all $Y$-columns"
**S**: (default: `" "`) Location to read matrix $S$ that has the stratum column.
Note: the stratum column must contain small positive integers; all
fractional values are rounded; stratum IDs of value ${\leq}\,0$ or NaN
-are treated as missing. The default value for S means “use
-$X$ in place of $S$”
+are treated as missing. The default value for S means "use
+$X$ in place of $S$"
**Scid**: (default: `1`) The index number of the stratum column in $S$
@@ -1318,9 +1318,9 @@ SystemML Language Reference for details.
</tr>
<tr>
<td>28</td>
- <td>p-value for “slope = 0”</td>
+ <td>p-value for "slope = 0"</td>
<td>38</td>
- <td>p-value for “slope = 0”</td>
+ <td>p-value for "slope = 0"</td>
</tr>
<tr>
<td>29</td>
@@ -1441,7 +1441,7 @@ $$
$$
where $V_x$, $V_y$, and $V_{x, y}$ are,
-correspondingly, the “stratified” sample estimates of variance
+correspondingly, the "stratified" sample estimates of variance
$Var(x)$ and
$Var(y)$ and covariance
$Cov(x,y)$ computed as
@@ -1484,10 +1484,10 @@ $$\hat{\beta}\, =\, \frac{V_{x,y}}{V_x}; \,\,\,\, \hat{R} \,=\, \frac{V_{x,y}}{\
\,\,\,\, \hat{\sigma} \,=\, \sqrt{\frac{\mathrm{RSS}}{n - k - 1}}\,\,\,\,
\Big(n = \sum_{i=1}^k n_i\Big)$$
-The $t$-test and the $F$-test for the null-hypothesis of “$\beta = 0$”
+The $t$-test and the $F$-test for the null-hypothesis of "$\beta = 0$"
are obtained by considering the effect of $\hat{\beta}$ on the residual
sum-of-squares, measured by the decrease from $V_y$ to RSS. The
-$F$-statistic is the ratio of the “explained” sum-of-squares to the
+$F$-statistic is the ratio of the "explained" sum-of-squares to the
residual sum-of-squares, divided by their corresponding degrees of
freedom. There are $n\,{-}\,k$ degrees of freedom for $V_y$, parameter
$\beta$ reduces that to $n\,{-}\,k\,{-}\,1$ for RSS, and their
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/135a0c0d/algorithms-regression.md
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diff --git a/algorithms-regression.md b/algorithms-regression.md
index f17c452..6472c17 100644
--- a/algorithms-regression.md
+++ b/algorithms-regression.md
@@ -54,7 +54,7 @@ $$
Distribution
$y_i \sim Normal(\mu_i, \sigma^2)$
-models the “unexplained” residual noise and is assumed independent
+models the "unexplained" residual noise and is assumed independent
across different records.
The goal is to estimate the regression coefficients and the residual
@@ -67,10 +67,10 @@ the response, left after prediction, with its total variance.
There are two scripts in our library, both doing the same estimation,
but using different computational methods. Depending on the size and the
sparsity of the feature matrix $X$, one or the other script may be more
-efficient. The “direct solve” script `LinearRegDS.dml` is more
+efficient. The "direct solve" script `LinearRegDS.dml` is more
efficient when the number of features $m$ is relatively small
($m \sim 1000$ or less) and matrix $X$ is either tall or fairly dense
-(has ${\gg}\:m^2$ nonzeros); otherwise, the “conjugate gradient” script
+(has ${\gg}\:m^2$ nonzeros); otherwise, the "conjugate gradient" script
`LinearRegCG.dml` is more efficient. If $m > 50000$, use only
`LinearRegCG.dml`.
@@ -370,7 +370,7 @@ to keep the intercept $\beta_0$ unregularized. If the intercept is
disabled by setting $icpt=0$, the equation is simply $X^T X \beta = X^T Y$.
We implemented two scripts for solving equation (4): one
-is a “direct solver” that computes $A$ and then solves
+is a "direct solver" that computes $A$ and then solves
$A\beta = [X,\,1]^T Y$ by calling an external package, the other
performs linear conjugate gradient (CG) iterations without ever
materializing $A$. The CG algorithm closely follows Algorithm 5.2 in
@@ -394,7 +394,7 @@ solver ($m \lesssim 50000$). A more precise determination between CG
and DS is subject to further research.
In addition to the $\beta$-vector, the scripts estimate the residual
-standard deviation $\sigma$ and the $R^2$, the ratio of “explained”
+standard deviation $\sigma$ and the $R^2$, the ratio of "explained"
variance to the total variance of the response variable. These
statistics only make sense if the number of degrees of freedom
$n\,{-}\,m\,{-}\,1$ is positive and the regularization constant
@@ -772,8 +772,8 @@ log link $\eta = \log\mu$. Common power links:
* 1.0 = identity
**yneg**: (default: `0.0`) When `dfam=2` and the response matrix $Y$ has
-1 column, this specifies the $y$-value used for Bernoulli “No” label
-(“Yes” is $1$):
+1 column, this specifies the $y$-value used for Bernoulli "No" label
+("Yes" is $1$):
0.0 when $y\in\\{0, 1\\}$; -1.0 when
$y\in\\{-1, 1\\}$
@@ -889,14 +889,14 @@ Iteration\#, Value) with Iteration\# being 0 for initial values.
| LINEAR\_TERM\_MIN | The minimum value of $X$ %\*% $\beta$, used to check for overflows
| LINEAR\_TERM\_MAX | The maximum value of $X$ %\*% $\beta$, used to check for overflows
| IS\_POINT\_UPDATED | 1 = new point accepted; 0 = new point rejected, old point restored
-| TRUST\_DELTA | Updated trust region size, the “delta”
+| TRUST\_DELTA | Updated trust region size, the "delta"
* * *
<a name="table11" />
-**Table 11**: Common GLM distribution families and link functions. (Here “\*” stands
-for “any value.”)
+**Table 11**: Common GLM distribution families and link functions. (Here "\*" stands
+for "any value.")
| dfam | vpow | link | lpow | Distribution<br />Family | Link<br /> Function | Canonical |
| :--: | :--: | :--: | :--: | :------------: | :--------: | :----: |
@@ -1213,7 +1213,7 @@ following link functions are supported:
* 3 = probit
* 4 = cloglog
-**yneg**: (default: `0.0`) Response value for Bernoulli “No” label, usually 0.0 or -1.0
+**yneg**: (default: `0.0`) Response value for Bernoulli "No" label, usually 0.0 or -1.0
**icpt**: (default: `0`) Intercept and shifting/rescaling of the features in $X$:
@@ -1454,7 +1454,7 @@ response means (for `dfam=1`) or probabilities (for
* $n {\times} 1$: for power-type distributions (`dfam=1`)
* $n {\times} 2$: for binomial distribution (`dfam=2`),
-col\# 2 is the “No” probability
+col\# 2 is the "No" probability
* $n {\times} k\,{+}\,1$: for multinomial logit (`dfam=3`),
col\# $k\,{+}\,1$ is for the baseline
@@ -1788,13 +1788,13 @@ unknown (which sets it to `1.0`).
<a name="table13" />
**Table 13**: The goodness-of-fit statistics are provided in CSV format, one per each line, with four
columns: (Name, CID, Disp?, Value). The columns are:
-“Name” is the string identifier for the statistic;
-“CID” is an optional integer value that specifies the $Y$-column index for per-column statistics
+"Name" is the string identifier for the statistic;
+"CID" is an optional integer value that specifies the $Y$-column index for per-column statistics
(note that a bi/multinomial one-column Y-input is converted into multi-column);
-“Disp?” is an optional Boolean value ($TRUE$ or $FALSE$) that tells us
+"Disp?" is an optional Boolean value ($TRUE$ or $FALSE$) that tells us
whether or not scaling by the input dispersion parameter `disp` has been applied to this
statistic;
-“Value” is the value of the statistic.
+"Value" is the value of the statistic.
| Name | CID | Disp? | Meaning |
| -------------------- | :-: | :---: | ------- |
@@ -1825,15 +1825,15 @@ The output matrix $M$ of predicted means (or probabilities) is computed
by matrix-multiplying $X$ with the first column of $B$ or with the
whole $B$ in the multinomial case, adding the intercept if available
(conceptually, appending an extra column of ones to $X$); then applying
-the inverse of the model’s link function. The difference between “means”
-and “probabilities” in the categorical case becomes significant when
+the inverse of the model’s link function. The difference between "means"
+and "probabilities" in the categorical case becomes significant when
there are ${\geq}\,2$ observations per record (with the multi-label
records) or when the labels such as $-1$ and $1$ are viewed and averaged
as numerical response values (with the single-label records). To avoid
any or information loss, we separately return the predicted probability
of each category label for each record.
-When the “actual” response values $Y$ are available, the summary
+When the "actual" response values $Y$ are available, the summary
statistics are computed and written out as described in
[**Table 13**](algorithms-regression.html#table13). Below we discuss each of these statistics
in detail. Note that in the categorical case (binomial and multinomial)
@@ -1845,7 +1845,7 @@ each response label is converted into an indicator vector
$(0,\ldots,0,1_l,0,\ldots,0)$ where $l$ is the label ID for this record.
All negative (e.g. $-1$) or zero label IDs are converted to the
$1 +$ maximum label ID. The largest label ID is viewed as the
-“baseline” as explained in the section on Multinomial Logistic
+"baseline" as explained in the section on Multinomial Logistic
Regression. We assume that there are $k\geq 1$ non-baseline categories
and one (last) baseline category.
@@ -1900,12 +1900,12 @@ Z \,=\, \frac {\ell(Y) - E_Y \ell(Y)}{\sqrt{Var_Y \ell(Y)}};\quad
\mathop{\textrm{p-value}}(Z) \,=\, Prob\Big[\,\big|\mathop{\textrm{Normal}}(0,1)\big| \, > \, |Z|\,\Big]
$$
-A low p-value would indicate “underfitting” if $Z\ll 0$ or “overfitting”
-if $Z\gg 0$. Here “overfitting” means that higher-probability labels
+A low p-value would indicate "underfitting" if $Z\ll 0$ or "overfitting"
+if $Z\gg 0$. Here "overfitting" means that higher-probability labels
occur more often than their probabilities suggest.
We also apply the dispersion input (`disp`) to compute the
-“scaled” version of the $Z$-score and its p-value. Since $\ell(Y)$ is a
+"scaled" version of the $Z$-score and its p-value. Since $\ell(Y)$ is a
linear function of $Y$, multiplying the GLM-predicted variance of $Y$ by
`disp` results in multiplying
$Var_Y \ell(Y)$ by the same
@@ -1954,11 +1954,11 @@ are unreliable (i.e. do not approach the $\chi^2$ distribution) unless
the predicted means of multi-label counts
$$\mu_{i,j} = N_i \hspace{0.5pt} p_{i,j}$$ are fairly large: all
${\geq}\,1$ and 80% are at least $5$ [[Cochran1954]](algorithms-bibliography.html). They should not
-be used for “one label per record” categoricals.
+be used for "one label per record" categoricals.
`DEVIANCE_G2`, `DEVIANCE_G2_BY_DF`, and
`DEVIANCE_G2_PVAL`: Deviance $G^2$ is the log of the
-likelihood ratio between the “saturated” model and the linear model
+likelihood ratio between the "saturated" model and the linear model
being tested for the given dataset, multiplied by two:
$$
@@ -1967,10 +1967,10 @@ G^2 \,=\, 2 \,\log \frac{Prob[Y \mid \textrm{saturated model}\hspace{0.5pt}]}{Pr
\end{equation}
$$
-The “saturated” model sets the mean
+The "saturated" model sets the mean
$\mu_i^{\mathrm{sat}}$ to equal $y_i$ for every record (for categorical
data, $$p_{i,j}^{sat} = y_{i,j} / N_i$$), which represents the
-“perfect fit.” For records with $y_{i,j} \in \{0, N_i\}$ or otherwise at
+"perfect fit." For records with $y_{i,j} \in \{0, N_i\}$ or otherwise at
a boundary, by continuity we set $0 \log 0 = 0$. The GLM likelihood
functions defined in (6) become simplified in
ratio (8) due to canceling out the term $c(y, a)$
@@ -1979,7 +1979,7 @@ since it is the same in both models.
The log of a likelihood ratio between two nested models, times two, is
known to approach a $\chi^2$ distribution as $n\to\infty$ if both models
have fixed parameter spaces. But this is not the case for the
-“saturated” model: it adds more parameters with each record. In
+"saturated" model: it adds more parameters with each record. In
practice, however, $\chi^2$ distributions are used to compute the
p-value of $G^2$ [[McCullagh1989]](algorithms-bibliography.html). The number of degrees of
freedom \#d.f. and the treatment of dispersion are the same as for
@@ -2032,7 +2032,7 @@ We also compute the $R^2$ statistics for each column of $Y$, see
of $R^2$: in one version the residual sum-of-squares (RSS) includes any
bias in the residual that might be present (due to the lack of, or
inaccuracy in, the intercept); in the other version of RSS the bias is
-subtracted by “centering” the residual. In both cases we subtract the
+subtracted by "centering" the residual. In both cases we subtract the
bias in the total sum-of-squares (in the denominator), and $m'$ equals
$m$ with the intercept or $m+1$ without the intercept.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/135a0c0d/dml-language-reference.md
----------------------------------------------------------------------
diff --git a/dml-language-reference.md b/dml-language-reference.md
index 20db3c2..8128871 100644
--- a/dml-language-reference.md
+++ b/dml-language-reference.md
@@ -60,16 +60,16 @@ limitations under the License.
## Introduction
-SystemML compiles scripts written in Declarative Machine Learning (or DML for short) into MapReduce jobs. DML’s syntax closely follows R, thereby minimizing the learning curve to use SystemML. Before getting into detail, let’s start with a simple Hello World program in DML. Assuming that Hadoop is installed on your machine or cluster, place SystemML.jar and SystemML-config.xml into your directory. Now, create a text file “hello.dml” containing following code:
+SystemML compiles scripts written in Declarative Machine Learning (or DML for short) into MapReduce jobs. DML’s syntax closely follows R, thereby minimizing the learning curve to use SystemML. Before getting into detail, let’s start with a simple Hello World program in DML. Assuming that Hadoop is installed on your machine or cluster, place SystemML.jar and SystemML-config.xml into your directory. Now, create a text file "hello.dml" containing following code:
- print(“Hello World”);
+ print("Hello World");
To run this program on your machine, use following command:
hadoop jar SystemML.jar –f hello.dml
-The option ```-f``` in the above command refers to the path to the DML script. The detailed list of the options is given in the section “Invocation of SystemML”. Note: to install Hadoop, please follow the instructions given at [http://ibm.co/1yHtrdb](http://ibm.co/1yHtrdb).
+The option ```-f``` in the above command refers to the path to the DML script. The detailed list of the options is given in the section "Invocation of SystemML". Note: to install Hadoop, please follow the instructions given at [http://ibm.co/1yHtrdb](http://ibm.co/1yHtrdb).
## Variables
@@ -89,7 +89,7 @@ Identifiers are case-sensitive (e.g., ```var1```, ```Var1```, and ```VAR1``` are
Before, proceeding ahead let’s run the Hello World program using variable:
- helloStr = “Hello World”
+ helloStr = "Hello World"
print(helloStr)
As seen in above example, there is no formal declaration of a variable. A variable is created when first assigned a value, and its type is inferred.
@@ -144,7 +144,7 @@ Table 1. Operators
| + - | Matrix or Scalar | Matrix or Scalar<sup>1, 2</sup> | Addition (or string concatenation) and Subtraction
| < > == != <= >= | Matrix or Scalar (any value type) | Scalar<sup>2</sup> (boolean type) | Relational operators
| & \| ! | Scalar | Scalar | Boolean operators (Note: operators && and \|\| are not supported)
-| = | - | - | Assignment (Lowest precendence). Note: associativity of assignment “a = b = 3” is not supported
+| = | - | - | Assignment (Lowest precendence). Note: associativity of assignment "a = b = 3" is not supported
1 If one of the operands is a matrix, output is matrix; otherwise it is scalar.
@@ -460,7 +460,7 @@ Note: The command-line parameters are treated as constants which are introduced
}
print("A:" + A);
-This will result in parser warning, but the program will run to completion. If the expression in the “if” predicate would have evaluated to false, it would have resulted in runtime error. Also, functions need not be defined prior to its call. That is: following code will work without parser warning:
+This will result in parser warning, but the program will run to completion. If the expression in the "if" predicate would have evaluated to false, it would have resulted in runtime error. Also, functions need not be defined prior to its call. That is: following code will work without parser warning:
A = 2;
C = foo(1, A)
@@ -490,13 +490,13 @@ Since most algorithms require arguments to be passed from command line, DML supp
1. As named arguments (recommended):
- ```-nvargs param1=7 param2=”abc” param3=3.14```
+ ```-nvargs param1=7 param2="abc" param3=3.14```
2. As positional arguments (deprecated):
- ```-args 7 “abc” 3.14```
+ ```-args 7 "abc" 3.14```
-The named arguments can be accessed by adding “\\$” before the parameter name, i.e. \\$param1. On the other hand, the positional parameter are accessible by adding “\\$” before their positions (starting from index 1), i.e. \\$1. A string parameter can be passed without quote. For example, ```param2=abc``` is valid argument, but it is not recommend.
+The named arguments can be accessed by adding "\\$" before the parameter name, i.e. \\$param1. On the other hand, the positional parameter are accessible by adding "\\$" before their positions (starting from index 1), i.e. \\$1. A string parameter can be passed without quote. For example, ```param2=abc``` is valid argument, but it is not recommend.
Sometimes the user would want to support default values in case user does not explicitly pass the corresponding command line parameter (in below example: ```$nbrRows```). To do so, we use the ```ifdef``` function which assigns either command line parameter or the default value to the local parameter.
@@ -509,13 +509,13 @@ Sometimes the user would want to support default values in case user does not ex
write (M, $fname, format="csv")
print("Done creating and writing random matrix in " + $fname)
-In above script, ```ifdef(\$nbrRows, 10)``` function is a short-hand for “```ifdef(\$nbrRows) then \$nbrRows else 10```”.
+In above script, ```ifdef(\$nbrRows, 10)``` function is a short-hand for "```ifdef(\$nbrRows) then \$nbrRows else 10```".
Let’s assume that the above script is invoked using following the command line values:
hadoop jar SystemML.jar -f test.dml -nvargs fname=test.mtx nbrRows=5 nbrCols=5
-In this case, the script will create a random matrix M with 5 rows and 5 columns and write it to the file “text.mtx” in csv format. After that it will print the message “Done creating and writing random matrix in test.mtx” on the standard output.
+In this case, the script will create a random matrix M with 5 rows and 5 columns and write it to the file "text.mtx" in csv format. After that it will print the message "Done creating and writing random matrix in test.mtx" on the standard output.
If however, the above script is invoked from the command line using named arguments:
@@ -536,7 +536,7 @@ Each argValue passed from the command-line has a scalar data type, and the value
else
Assign argValue string value type
-In above example, the placeholder variable ```\$nbrCols``` will be treated as integer in the script. If however, the command line arguments were “```nbrCols=5.0```”, then it would be treated as a double.
+In above example, the placeholder variable ```\$nbrCols``` will be treated as integer in the script. If however, the command line arguments were "```nbrCols=5.0```", then it would be treated as a double.
NOTE: argName must be a valid identifier.
NOTE: If argValue contains spaces, it must be enclosed in double-quotes.
@@ -581,7 +581,7 @@ min() <br/> max() | Return the minimum/maximum cell value in matrix | Input: mat
min() <br/> max() | Return the minimum/maximum cell values of two matrices, matrix and scalar, or scalar value of two scalars. | Input: matrices or scalars <br/> Output: matrix or scalar | With x,y, z as scalars, and X, Y, Z as matrices: <br/> Z = min (X, Y) <br/> Z = min (X, y) <br/> z = min(x,y)
nrow(), <br/> ncol(), <br/> length() | Return the number of rows, number of columns, or number of cells in matrix respectively. | Input: matrix <br/> Output: scalar | nrow(X)
prod() | Return the product of all cells in matrix | Input: matrix <br/> Output: scalarj | prod(X)
-rand() | Generates a random matrix | Input: (rows=<value>, cols=<value>, min=<value>, max=<value>, sparsity=<value>, pdf=<string>, seed=<value>) <br/> rows/cols: Number of rows/cols (expression) <br/> min/max: Min/max value for cells (either constant value, or variable that evaluates to constant value) <br/> sparsity: fraction of non-zero cells (constant value) <br/> pdf: “uniform” (min, max) distribution, or “normal” (0,1) distribution; or “poisson” (lambda=1) distribution. string; default value is “uniform”. Note that, for the Poisson distribution, users can provide the mean/lambda parameter as follows: <br/> rand(rows=1000,cols=1000, pdf=”poisson”, lambda=2.5). <br/> The default value for lambda is 1. <br/> seed: Every invocation of rand() internally generates a random seed with which the cell values are generated. One can optionally provide a seed when repeatability is desired. <br/> Output: matrix | X = rand(rows=1
0, cols=20, min=0, max=1, pdf="uniform", sparsity=0.2) <br/> The example generates a 10 x 20 matrix, with cell values uniformly chosen at random between 0 and 1, and approximately 20% of cells will have non-zero values.
+rand() | Generates a random matrix | Input: (rows=<value>, cols=<value>, min=<value>, max=<value>, sparsity=<value>, pdf=<string>, seed=<value>) <br/> rows/cols: Number of rows/cols (expression) <br/> min/max: Min/max value for cells (either constant value, or variable that evaluates to constant value) <br/> sparsity: fraction of non-zero cells (constant value) <br/> pdf: "uniform" (min, max) distribution, or "normal" (0,1) distribution; or "poisson" (lambda=1) distribution. string; default value is "uniform". Note that, for the Poisson distribution, users can provide the mean/lambda parameter as follows: <br/> rand(rows=1000,cols=1000, pdf="poisson", lambda=2.5). <br/> The default value for lambda is 1. <br/> seed: Every invocation of rand() internally generates a random seed with which the cell values are generated. One can optionally provide a seed when repeatability is desired. <br/> Output: matrix | X = rand(rows=10, cols=20, min=0, m
ax=1, pdf="uniform", sparsity=0.2) <br/> The example generates a 10 x 20 matrix, with cell values uniformly chosen at random between 0 and 1, and approximately 20% of cells will have non-zero values.
rbind() | Row-wise matrix concatenation. Concatenates the second matrix as additional rows to the first matrix | Input: (X <matrix>, Y <matrix>) <br/>Output: <matrix> <br/> X and Y are matrices, where the number of columns in X and the number of columns in Y are the same. | A = matrix(1, rows=2,cols=3) <br/> B = matrix(2, rows=2,cols=3) <br/> C = rbind(A,B) <br/> print("Dimensions of C: " + nrow(C) + " X " + ncol(C)) <br/> Output: <br/> Dimensions of C: 4 X 3
removeEmpty() | Removes all empty rows or columns from the input matrix target X according to the specified margin. | Input : (target= X <matrix>, margin="...") <br/> Output : <matrix> <br/> Valid values for margin are "rows" or "cols". | A = removeEmpty(target=X, margin="rows")
replace() | Creates a copy of input matrix X, where all values that are equal to the scalar pattern s1 are replaced with the scalar replacement s2. | Input : (target= X <matrix>, pattern=<scalar>, replacement=<scalar>) <br/> Output : <matrix> <br/> If s1 is NaN, then all NaN values of X are treated as equal and hence replaced with s2. Positive and negative infinity are treated as different values. | A = replace(target=X, pattern=s1, replacement=s2)
@@ -619,8 +619,8 @@ moment() | Returns the kth central moment of values in a column matrix V, where
colSums() <br/> colMeans() <br/> colMaxs() <br/> colMins() | Column-wise computations -- for each column, compute the sum/mean/max/min of cell values | Input: matrix <br/> Output: (1 x n) matrix | colSums(X) <br/> colMeans(X) <br/> colMaxs(X) <br/>colMins(X)
cov() | Returns the covariance between two 1-dimensional column matrices X and Y. The function takes an optional weights parameter W. All column matrices X, Y, and W (when specified) must have the exact same dimension. | Input: (X <(n x 1) matrix>, Y <(n x 1) matrix> [, W <(n x 1) matrix>)]) <br/> Output: <scalar> | cov(X,Y) <br/> cov(X,Y,W)
table() | Returns the contingency table of two vectors A and B. The resulting table F consists of max(A) rows and max(B) columns. <br/> More precisely, F[i,j] = \\|{ k \\| A[k] = i and B[k] = j, 1 ≤ k ≤ n }\\|, where A and B are two n-dimensional vectors. <br/> This function supports multiple other variants, which can be found below, at the end of this Table 6. | Input: (<(n x 1) matrix>, <(n x 1) matrix>), [<(n x 1) matrix>]) <br/> Output: <matrix> | F = table(A, B) <br/> F = table(A, B, C) <br/> And, several other forms (see below Table 6.)
-cdf()<br/> pnorm()<br/> pexp()<br/> pchisq()<br/> pf()<br/> pt()<br/> icdf()<br/> qnorm()<br/> qexp()<br/> qchisq()<br/> qf()<br/> qt() | p=cdf(target=q, ...) returns the cumulative probability P[X <= q]. <br/> q=icdf(target=p, ...) returns the inverse cumulative probability i.e., it returns q such that the given target p = P[X<=q]. <br/> For more details, please see the section “Probability Distribution Functions” below Table 6. | Input: (target=<scalar>, dist="...", ...) <br/> Output: <scalar> | p = cdf(target=q, dist="normal", mean=1.5, sd=2); is same as p=pnorm(target=q, mean=1.5, sd=2); <br/> q=icdf(target=p, dist=”normal”) is same as q=qnorm(target=p, mean=0,sd=1) <br/> More examples can be found in the section “Probability Distribution Functions” below Table 6.
-aggregate() | Splits/groups the values from X according to the corresponding values from G, and then applies the function fn on each group. <br/> The result F is a column matrix, in which each row contains the value computed from a distinct group in G. More specifically, F[k,1] = fn( {X[i,1] \\| 1<=i<=n and G[i,1] = k} ), where n = nrow(X) = nrow(G). <br/> Note that the distinct values in G are used as row indexes in the result matrix F. Therefore, nrow(F) = max(G). It is thus recommended that the values in G are consecutive and start from 1. <br/> This function supports multiple other variants, which can be found below, at the end of this Table 6. | Input: (target= X <(n x 1) matrix>, groups = G <(n x 1) matrix>, fn="..." [,weights= W<(n x 1) matrix>]) <br/> Output: F <matrix> <br/> The parameter fn takes one of the following functions: "count", "sum", "mean", "variance", "centralmoment”. In the case of central moment, one must also provide the ord
er of the moment that need to be computed (see example). | F = aggregate(target=X, groups=G, fn="..." [,weights = W]) <br/> F = aggregate(target=X, groups=G1, fn="sum"); <br/> F = aggregate(target=Y, groups=G2, fn="mean", weights=W); <br/> F = aggregate(target=Z, groups=G3, fn="centralmoment", order="2"); <br/> And, several other forms (see below Table 6.)
+cdf()<br/> pnorm()<br/> pexp()<br/> pchisq()<br/> pf()<br/> pt()<br/> icdf()<br/> qnorm()<br/> qexp()<br/> qchisq()<br/> qf()<br/> qt() | p=cdf(target=q, ...) returns the cumulative probability P[X <= q]. <br/> q=icdf(target=p, ...) returns the inverse cumulative probability i.e., it returns q such that the given target p = P[X<=q]. <br/> For more details, please see the section "Probability Distribution Functions" below Table 6. | Input: (target=<scalar>, dist="...", ...) <br/> Output: <scalar> | p = cdf(target=q, dist="normal", mean=1.5, sd=2); is same as p=pnorm(target=q, mean=1.5, sd=2); <br/> q=icdf(target=p, dist="normal") is same as q=qnorm(target=p, mean=0,sd=1) <br/> More examples can be found in the section "Probability Distribution Functions" below Table 6.
+aggregate() | Splits/groups the values from X according to the corresponding values from G, and then applies the function fn on each group. <br/> The result F is a column matrix, in which each row contains the value computed from a distinct group in G. More specifically, F[k,1] = fn( {X[i,1] \\| 1<=i<=n and G[i,1] = k} ), where n = nrow(X) = nrow(G). <br/> Note that the distinct values in G are used as row indexes in the result matrix F. Therefore, nrow(F) = max(G). It is thus recommended that the values in G are consecutive and start from 1. <br/> This function supports multiple other variants, which can be found below, at the end of this Table 6. | Input: (target= X <(n x 1) matrix>, groups = G <(n x 1) matrix>, fn="..." [,weights= W<(n x 1) matrix>]) <br/> Output: F <matrix> <br/> The parameter fn takes one of the following functions: "count", "sum", "mean", "variance", "centralmoment". In the case of central moment, one must also provide the order
of the moment that need to be computed (see example). | F = aggregate(target=X, groups=G, fn="..." [,weights = W]) <br/> F = aggregate(target=X, groups=G1, fn="sum"); <br/> F = aggregate(target=Y, groups=G2, fn="mean", weights=W); <br/> F = aggregate(target=Z, groups=G3, fn="centralmoment", order="2"); <br/> And, several other forms (see below Table 6.)
interQuartileMean() | Returns the mean of all x in X such that x>quantile(X, 0.25) and x<=quantile(X, 0.75). X, W are column matrices (vectors) of the same size. W contains the weights for data in X. | Input: (X <(n x 1) matrix> [, W <(n x 1) matrix>)]) <br/> Output: <scalar> | interQuartileMean(X) <br/> interQuartileMean(X, W)
quantile () | The p-quantile for a random variable X is the value x such that Pr[X<x] <= p and Pr[X<= x] >= p <br/> let n=nrow(X), i=ceiling(p*n), quantile() will return X[i]. p is a scalar (0<p<1) that specifies the quantile to be computed. Optionally, a weight vector may be provided for X. | Input: (X <(n x 1) matrix>, [W <(n x 1) matrix>),] p <scalar>) <br/> Output: <scalar> | quantile(X, p) <br/> quantile(X, W, p)
quantile () | Returns a column matrix with list of all quantiles requested in P. | Input: (X <(n x 1) matrix>, [W <(n x 1) matrix>),] P <(q x 1) matrix>) <br/> Output: matrix | quantile(X, P) <br/> quantile(X, W, P)
@@ -631,7 +631,7 @@ cumprod() | Column prefix-prod (For row-prefix prod, use cumprod(t(X)) | Input:
cummin() | Column prefix-min (For row-prefix min, use cummin(t(X)) | Input: matrix <br/> Output: matrix of the same dimensions | A = matrix("3 4 1 6 5 2", rows=3, cols=2) <br/> B = cummin(A) <br/> The output matrix B = [[3, 4], [1, 4], [1, 2]]
cummax() | Column prefix-max (For row-prefix min, use cummax(t(X)) | Input: matrix <br/> Output: matrix of the same dimensions | A = matrix("3 4 1 6 5 2", rows=3, cols=2) <br/> B = cummax(A) <br/> The output matrix B = [[3, 4], [3, 6], [5, 6]]
sample(range, size, replacement, seed) | Sample returns a column vector of length size, containing uniform random numbers from [1, range] | Input: <br/> range: integer <br/> size: integer <br/> replacement: boolean (Optional, default: FALSE) <br/> seed: integer (Optional) <br/> Output: Matrix dimensions are size x 1 | sample(100, 5) <br/> sample(100, 5, TRUE) <br/> sample(100, 120, TRUE) <br/> sample(100, 5, 1234) # 1234 is the seed <br/> sample(100, 5, TRUE, 1234)
-outer(vector1, vector2, “op”) | Applies element wise binary operation “op” (for example: “<”, “==”, “>=”, “*”, “min”) on the all combination of vector. <br/> Note: Using “*”, we get outer product of two vectors. | Input: vectors of same size d, string <br/> Output: matrix of size d X d | A = matrix("1 4", rows = 2, cols = 1) <br/> B = matrix("3 6", rows = 1, cols = 2) <br/> C = outer(A, B, "<") <br/> D = outer(A, B, "*") <br/> The output matrix C = [[1, 1], [0, 1]] <br/> The output matrix D = [[3, 6], [12, 24]]<br/>
+outer(vector1, vector2, "op") | Applies element wise binary operation "op" (for example: "<", "==", ">=", "*", "min") on the all combination of vector. <br/> Note: Using "*", we get outer product of two vectors. | Input: vectors of same size d, string <br/> Output: matrix of size d X d | A = matrix("1 4", rows = 2, cols = 1) <br/> B = matrix("3 6", rows = 1, cols = 2) <br/> C = outer(A, B, "<") <br/> D = outer(A, B, "*") <br/> The output matrix C = [[1, 1], [0, 1]] <br/> The output matrix D = [[3, 6], [12, 24]]<br/>
#### Alternative forms of table()
@@ -654,14 +654,14 @@ The built-in function table() supports different types of input parameters. Thes
The built-in function aggregate() supports different types of input parameters. These variations are described below:
- * Basic form: ```F=aggregate(target=X, groups=G, fn=”sum”)```
+ * Basic form: ```F=aggregate(target=X, groups=G, fn="sum")```
As described above in Table 6.
- * Weighted form: ```F=aggregate(target=X, groups=G, weights=W, fn=”sum”) ```
+ * Weighted form: ```F=aggregate(target=X, groups=G, weights=W, fn="sum") ```
Users can provide an optional parameter W with the same dimensions as of A and B. In this case, fn computes the weighted statistics over values from X, which are grouped by values from G.
* Specified Output Size
As noted in Table 6, the number of rows in the output matrix F is equal to the maximum value in the grouping matrix G. Therefore, the dimensions of F are known only after its execution is complete. When needed, users can precisely control the size of the output matrix via an additional argument, ```ngroups```, as shown below: <br/>
- ```F = aggregate(target=X, groups=G, fn=”sum”, ngroups=10);``` <br/>
-The output F will have exactly 10 rows and 1 column. F may be a truncated or padded (with zeros) version of the output produced by ```aggregate(target=X, groups=G, fn=”sum”)``` – depending on the values of ```ngroups``` and ```max(G)```. For example, if ```max(G) < ngroups``` then the last (```ngroups-max(G)```) rows will have zeros.
+ ```F = aggregate(target=X, groups=G, fn="sum", ngroups=10);``` <br/>
+The output F will have exactly 10 rows and 1 column. F may be a truncated or padded (with zeros) version of the output produced by ```aggregate(target=X, groups=G, fn="sum")``` – depending on the values of ```ngroups``` and ```max(G)```. For example, if ```max(G) < ngroups``` then the last (```ngroups-max(G)```) rows will have zeros.
#### Probability Distribution Functions
@@ -670,12 +670,12 @@ The output F will have exactly 10 rows and 1 column. F may be a truncated or pad
This computes the cumulative probability at the given quantile i.e., P[X<=q], where X is random variable whose distribution is specified via string argument fn.
* ```target```: input quantile at which cumulative probability P[X<=q] is computed, where X is random variable whose distribution is specified via string argument fn. This is a mandatory argument.
- * ```dist```: name of the distribution specified as a string. Valid values are “normal” (for Normal or Gaussian distribution), “f” (for F distribution), “t” (for Student t-distribution), “chisq” (for Chi Squared distribution), and “exp” (for Exponential distribution). This is a mandatory argument.
+ * ```dist```: name of the distribution specified as a string. Valid values are "normal" (for Normal or Gaussian distribution), "f" (for F distribution), "t" (for Student t-distribution), "chisq" (for Chi Squared distribution), and "exp" (for Exponential distribution). This is a mandatory argument.
* ```...```: parameters of the distribution
- * For ```dist=”normal”```, valid parameters are mean and sd that specify the mean and standard deviation of the normal distribution. The default values for mean and sd are 0.0 and 1.0, respectively.
- * For ```dist=”f”```, valid parameters are df1 and df2 that specify two degrees of freedom. Both these parameters are mandatory.
- * For ```dist=”t”```, and dist=”chisq”, valid parameter is df that specifies the degrees of freedom. This parameter is mandatory.
- * For ```dist=”exp”```, valid parameter is rate that specifies the rate at which events occur. Note that the mean of exponential distribution is 1.0/rate. The default value is 1.0.
+ * For ```dist="normal"```, valid parameters are mean and sd that specify the mean and standard deviation of the normal distribution. The default values for mean and sd are 0.0 and 1.0, respectively.
+ * For ```dist="f"```, valid parameters are df1 and df2 that specify two degrees of freedom. Both these parameters are mandatory.
+ * For ```dist="t"```, and dist="chisq", valid parameter is df that specifies the degrees of freedom. This parameter is mandatory.
+ * For ```dist="exp"```, valid parameter is rate that specifies the rate at which events occur. Note that the mean of exponential distribution is 1.0/rate. The default value is 1.0.
* ```Lower.tail```: a Boolean value with default set to TRUE. cdf() computes P[X<=q] when lower.tail=TRUE and it computes P[X>q] when lower.tail=FALSE. In other words, a complement of the cumulative distribution is computed when lower.tail=FALSE.
##### ```q = icdf(target=p, dist=fn, ...)```
@@ -720,23 +720,23 @@ is same as
Examples of icdf():
-```q=icdf(target=p, dist=”normal”);```
+```q=icdf(target=p, dist="normal");```
is same as
```q=qnorm(target=p, mean=0,sd=1);```
-```q=icdf(target=p, dist=”exp”);```
+```q=icdf(target=p, dist="exp");```
is same as
```q=qexp(target=p, rate=1);```
-```q=icdf(target=p, dist=”chisq”, df=50);```
+```q=icdf(target=p, dist="chisq", df=50);```
is same as
```qchisq(target=p, df=50);```
-```q=icdf(target=p, dist=”f”, df1=50, df2=25);```
+```q=icdf(target=p, dist="f", df1=50, df2=25);```
is same as
```qf(target=p, , df1=50, df2=25);```
-```q=icdf(target=p, dist=”t”, df=50);```
+```q=icdf(target=p, dist="t", df=50);```
is same as
```qt(target=p, df=50);```
@@ -813,8 +813,8 @@ Parameter Name | Description | Optional | Permissible values | Data type valid f
-------------- | ----------- | -------- | ------------------ | -------------------
data_type | Indicates the data type of the data | Yes. Default value is matrix if not specified. | matrix, scalar. | matrix, scalar.
value_type | Indicates the value type of data | Yes. Default value is double if not specified | double, int, string, boolean (Not case-sensitive). Must be double when data_type matrix | matrix, scalar.
-rows | Number of rows in matrix | Yes – only when format=”csv”. | Any integer > 0 | matrix
-cols | Number of columns in matrix | Yes – only when format=”csv” | Any integer > 0 | matrix
+rows | Number of rows in matrix | Yes – only when format="csv". | Any integer > 0 | matrix
+cols | Number of columns in matrix | Yes – only when format="csv" | Any integer > 0 | matrix
rows_in_block, cols_in_block | Valid only for binary blocked matrix. Indicate dimensions of blocks | No. Only valid if matrix in binary blocked format. | Any integer > 0 | matrix in blocked binary format. Valid only when format "binary"
nnz | Number of non-zero values | Yes | Any integer > 0 | matrix
format | data file format | Yes. Default value is text | binary, text, mm, csv | matrix, scalar. Formats mm and csv are applicable only for matrices.
@@ -828,7 +828,7 @@ Parameter Name | Description | Optional | Permissible values | Data type valid f
-------------- | ----------- | -------- | ------------------ | -------------------
header | Specifies whether the input data file has a header. Header, if exists, must be a single line and it must also be the first line in the file. | Yes, default value is False. | true or false | Matrix
sep | Specifies the separator or the delimiter used in the input data file. Note that using a delimiter composed of just numeric values or a dot (decimal point) can be ambiguous and may lead to unexpected results. | Yes, default value is ‘,’ (comma) | A quoted string | matrix
-fill | Only valid when reading CSV files. It specifies whether or not to fill the empty fields in the input file. Empty fields are denoted by consecutive separators/delimiters. If fill=true then every empty field is filled with the value specified by “default” attribute. An exception is raised if fill=false and and the input file has one or more empty fields. | Yes, default is true. | true or false | matrix
+fill | Only valid when reading CSV files. It specifies whether or not to fill the empty fields in the input file. Empty fields are denoted by consecutive separators/delimiters. If fill=true then every empty field is filled with the value specified by "default" attribute. An exception is raised if fill=false and and the input file has one or more empty fields. | Yes, default is true. | true or false | matrix
default | Only valid when reading CSV files and fill=true. It specifies the special value with which all empty values are filled while reading the input matrix. | Yes, default value is 0 (zero) | Any double value | matrix
sparse | Only valid when writing CSV files. It specifies whether or not to explicitly output zero (0) values. Zero values are written out only when sparse=false. | Yes, default value is true. | true or false | matrix
@@ -842,9 +842,9 @@ Furthermore, the following additional notes apply when reading and writing CSV f
The syntax of a read statement is as follows:
- read(“inputfile”, [list of parameters])
+ read("inputfile", [list of parameters])
-where inputfile is the path to the data file in HDFS. The list of parameters is the same as provided in MTD files. For "inputfile" parameter, the user can use constant string concatenation to give the full path of the file, where "+" is used as the concatenation operator. However, the file path must evaluate to a constant string at compile time. For example, “/my/dir”+“filename.mtx” is valid parameter but “/my/dir”+“filename”+i+“.mtx” is not (where i is a variable).
+where inputfile is the path to the data file in HDFS. The list of parameters is the same as provided in MTD files. For "inputfile" parameter, the user can use constant string concatenation to give the full path of the file, where "+" is used as the concatenation operator. However, the file path must evaluate to a constant string at compile time. For example, "/my/dir"+"filename.mtx" is valid parameter but "/my/dir"+"filename"+i+".mtx" is not (where i is a variable).
The user has the option of specifying each parameter value either in the MTD file, the read function invocation, or in both locations. **However, parameter values specified in both the read invocation and MTD file must have the same value. Also, if a scalar value is being read, then format cannot be specified.** The read invocation in SystemML is parameterized as follows during compilation.
@@ -859,7 +859,7 @@ The user has the option of specifying each parameter value either in the MTD fil
# Defaults for data_type and value_type are used.
V = read ("in/v", rows=10, cols=8, format="text");
# read a matrix from HDFS with path "in/v".
- # The user specifies "in" as the directory and “v” as
+ # The user specifies "in" as the directory and "v" as
# the file name and uses constant string concatenation.
dir = "in";
file = "v";
@@ -868,7 +868,7 @@ The user has the option of specifying each parameter value either in the MTD fil
# (MTD file path: in/data.mtd)
V = read("in/data");
# read a scalar integer value from HDFS file "in/scalar_i"
- V = read(“in/scalar_i”,data_type=”scalar", value_type="int");
+ V = read("in/scalar_i",data_type="scalar", value_type="int");
Additionally, readMM() and read.csv() are supported and can be used instead of specifying format="mm", or format="csv" in the read() function.
@@ -876,9 +876,9 @@ Additionally, readMM() and read.csv() are supported and can be used instead of s
The write() method is used to persist scalar and matrix data to files in HDFS. The syntax of write() is below. The parameters are described in the table. Note, that the set of supported parameters for write() is NOT the same as for read(). SystemML writes a MTD file for the written data.
- write(identifier, “outputfile”, format = “fmt”)
+ write(identifier, "outputfile", format = "fmt")
-The user can use constant string concatenation in “outputfile” parameter to give the full path of the file, where "+" is used as the concatenation operator.
+The user can use constant string concatenation in "outputfile" parameter to give the full path of the file, where "+" is used as the concatenation operator.
Table 11. Parameters for ```write()``` method
@@ -891,7 +891,7 @@ format | Valid for all three datatypes. <br/> Indicates the output file format.
##### Examples
- # write V to HDFS file “out/file”, in text format.
+ # write V to HDFS file "out/file", in text format.
# Create MTD file out/file.mtd
write(V, "out/file");
@@ -903,13 +903,13 @@ out/file.mtd:
"rows": 10,
"cols": 8,
"nnz": 4,
- "format": "text”,
+ "format": "text",
"description": { "author": "SystemML" }
}
-Write V to HDFS file”out/file” in binary blocked format:
+Write V to HDFS file"out/file" in binary blocked format:
- write(V, “out/file”, format=”binary”);
+ write(V, "out/file", format="binary");
out/file.mtd:
@@ -925,7 +925,7 @@ out/file.mtd:
"description": { "author": "SystemML" }
}
-Write a scalar integer value to HDFS file “out/scalar_i”
+Write a scalar integer value to HDFS file "out/scalar_i"
write(x, "out/scalar_i");
@@ -1234,8 +1234,8 @@ Table 14. Other Built-In Functions
Function | Description | Parameters | Example
-------- | ----------- | ---------- | -------
append() | Append a string to another string separated by "\n" <br/> Limitation: The string may grow up to 1 MByte. | Input: (<string>, <string>) <br/> Output: <string> | s = "iter=" + i <br/> i = i + 1 <br/> s = append(s, "iter=" + i) <br/> write(s, "s.out")
-print() | Prints the value of a scalar variable x. This built-in takes an optional string parameter. | Input: (<scalar>) | print(“hello”) <br/> print(“hello” + “world”) <br/> print("value of x is " + x )
-stop() | Halts the execution of DML program by printing the message that is passed in as the argument. <br/> Note that the use of stop() is not allowed inside a parfor loop. | Input: (<scalar>) | stop(“Inputs to DML program are invalid”) <br/> stop(“Class labels must be either -1 or +1”)
+print() | Prints the value of a scalar variable x. This built-in takes an optional string parameter. | Input: (<scalar>) | print("hello") <br/> print("hello" + "world") <br/> print("value of x is " + x )
+stop() | Halts the execution of DML program by printing the message that is passed in as the argument. <br/> Note that the use of stop() is not allowed inside a parfor loop. | Input: (<scalar>) | stop("Inputs to DML program are invalid") <br/> stop("Class labels must be either -1 or +1")
order() | Sort a column of the matrix X in decreasing/increasing order and return either index (indexreturn=TRUE) or data (indexreturn=FALSE). | Input: (target=X, by=column, decreasing, indexreturn) | order(X, by=1, decreasing=FALSE, indexreturn=FALSE)
@@ -1363,7 +1363,7 @@ First, you need to call SystemML using –debug flag.
hadoop jar SystemML.jar -f test.dml –debug
-You can see the line numbers in your DML script by “list” (or simply “l”) command:
+You can see the line numbers in your DML script by "list" (or simply "l") command:
(SystemMLdb) l
line 1: A = matrix("1 2 3 4 5 6", rows=3, cols=2)
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/135a0c0d/quick-start-guide.md
----------------------------------------------------------------------
diff --git a/quick-start-guide.md b/quick-start-guide.md
index cee5924..6c60fee 100644
--- a/quick-start-guide.md
+++ b/quick-start-guide.md
@@ -226,7 +226,7 @@ univariate statistic.
The following table lists the number and name of each univariate statistic. The row
numbers below correspond to the elements of the first column in the output
-matrix above. The signs “+” show applicability to scale or/and to categorical
+matrix above. The signs "+" show applicability to scale or/and to categorical
features.
| Row | Name of Statistic | Scale | Categ. |
[2/4] incubator-systemml git commit: change quotation mark from utf-8
to ascii
Posted by de...@apache.org.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/135a0c0d/Language Reference/SystemML_Language_Reference.html
----------------------------------------------------------------------
diff --git a/Language Reference/SystemML_Language_Reference.html b/Language Reference/SystemML_Language_Reference.html
deleted file mode 100644
index b442da3..0000000
--- a/Language Reference/SystemML_Language_Reference.html
+++ /dev/null
@@ -1,9804 +0,0 @@
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-
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- <w:BreakWrappedTables/>
- <w:SnapToGridInCell/>
- <w:WrapTextWithPunct/>
- <w:UseAsianBreakRules/>
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- <w:SplitPgBreakAndParaMark/>
- <w:DontVertAlignCellWithSp/>
- <w:DontBreakConstrainedForcedTables/>
- <w:DontVertAlignInTxbx/>
- <w:Word11KerningPairs/>
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-
-<p class=MsoTitle>SystemML Language Reference</p>
-
-</div>
-
-<p class=MsoSubtitle><a name=mozTocId182601></a>August 2014<o:p></o:p></p>
-
-<w:sdt sdtdocpart="t" docparttype="Table of Contents" docpartunique="t" id="472146699">
-
-<p class=MsoTocHeading>Table of Contents<w:sdtpr></w:sdtpr></p>
-
-<p class=MsoToc1 style='tab-stops:right dotted 467.5pt'><!--[if supportFields]><span
-style='mso-element:field-begin'></span><span
-style='mso-spacerun:yes'>�</span>TOC \o "1-3" \h \z \u <span
-style='mso-element:field-separator'></span><![endif]--><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846316">Introduction<span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846316 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>2<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300310036000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc1 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846317">Variables<span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846317 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>3<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300310037000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846318">Identifier
-Names<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846318 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>3<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300310038000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846319">Data
-Types<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846319 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>3<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300310039000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
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-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846320">Comments<span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846320 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>4<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320030000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
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-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc1 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846321">Expressions<span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846321 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>4<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320031000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846322">Operators<span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846322 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>4<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320032000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846323">Matrix-Vector
-operations<span style='color:windowtext;display:none;mso-hide:screen;
-text-decoration:none;text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846323 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>5<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320033000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846324">Matrix
-Indexing<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846324 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>6<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320034000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc1 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846325">Statements<span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846325 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>7<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320035000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846326">Assignment
-Statement<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'> </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846326 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>7<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320036000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846327">Control
-Statements<span style='color:windowtext;display:none;mso-hide:screen;
-text-decoration:none;text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846327 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>7<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320037000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc3 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846328">While
-Statement<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'> </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846328 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>7<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320038000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc3 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846329">If
-Statement<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'> </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846329 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>8<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300320039000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc3 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846330">For
-Statement<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'> </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846330 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>9<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300330030000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc3 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846331">ParFor
-Statement<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'> </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846331 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>9<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300330031000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc2 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846332">User-defined
-Function (UDF)<span style='color:windowtext;display:none;mso-hide:screen;
-text-decoration:none;text-underline:none'><span style='mso-tab-count:1 dotted'> </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846332 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>10<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300330032000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc1 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846333">Variable
-Scoping<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846333 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>12<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300330033000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc1 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846334">Command-line
-arguments<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846334 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>13<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300330034000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc1 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846335">Built-in
-Functions<span style='color:windowtext;display:none;mso-hide:screen;text-decoration:
-none;text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846335 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>15<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003400320035003800340036003300330035000000</w:data>
-</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
-display:none;mso-hide:screen;text-decoration:none;text-underline:none'><span
-style='mso-element:field-end'></span></span><![endif]--></a></span></span><span
-style='font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:
-minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
-minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
-mso-bidi-theme-font:minor-bidi;mso-no-proof:yes'><o:p></o:p></span></p>
-
-<p class=MsoToc3 style='tab-stops:right dotted 467.5pt'><span
-class=MsoHyperlink><span style='mso-no-proof:yes'><a href="#_Toc425846336">Matrix
-Construction, Manipulation, and Aggregation Built-In Functions<span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-tab-count:1 dotted'>. </span></span><!--[if supportFields]><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'><span style='mso-element:field-begin'></span> PAGEREF
-_Toc425846336 \h <span style='mso-element:field-separator'></span></span><![endif]--><span
-style='color:windowtext;display:none;mso-hide:screen;text-decoration:none;
-text-underline:none'>16<!--[if gte mso 9]><xml>
- <w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F
<TRUNCATED>
[4/4] incubator-systemml git commit: change returnindex to
return.index
Posted by de...@apache.org.
change returnindex to return.index
Closes #31.
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/4193b47c
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/4193b47c
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/4193b47c
Branch: refs/heads/gh-pages
Commit: 4193b47c27491874c365d1986b3292038a091110
Parents: 135a0c0
Author: Tommy YU <tu...@163.com>
Authored: Mon Dec 21 15:22:59 2015 +0800
Committer: Deron Eriksson <de...@us.ibm.com>
Committed: Mon Jan 4 12:02:44 2016 -0800
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dml-language-reference.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
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http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/4193b47c/dml-language-reference.md
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diff --git a/dml-language-reference.md b/dml-language-reference.md
index 8128871..f72dfaa 100644
--- a/dml-language-reference.md
+++ b/dml-language-reference.md
@@ -1236,7 +1236,7 @@ Function | Description | Parameters | Example
append() | Append a string to another string separated by "\n" <br/> Limitation: The string may grow up to 1 MByte. | Input: (<string>, <string>) <br/> Output: <string> | s = "iter=" + i <br/> i = i + 1 <br/> s = append(s, "iter=" + i) <br/> write(s, "s.out")
print() | Prints the value of a scalar variable x. This built-in takes an optional string parameter. | Input: (<scalar>) | print("hello") <br/> print("hello" + "world") <br/> print("value of x is " + x )
stop() | Halts the execution of DML program by printing the message that is passed in as the argument. <br/> Note that the use of stop() is not allowed inside a parfor loop. | Input: (<scalar>) | stop("Inputs to DML program are invalid") <br/> stop("Class labels must be either -1 or +1")
-order() | Sort a column of the matrix X in decreasing/increasing order and return either index (indexreturn=TRUE) or data (indexreturn=FALSE). | Input: (target=X, by=column, decreasing, indexreturn) | order(X, by=1, decreasing=FALSE, indexreturn=FALSE)
+order() | Sort a column of the matrix X in decreasing/increasing order and return either index (index.return=TRUE) or data (index.return=FALSE). | Input: (target=X, by=column, decreasing, index.return) | order(X, by=1, decreasing=FALSE, index.return=FALSE)
* * *
[3/4] incubator-systemml git commit: change quotation mark from utf-8
to ascii
Posted by de...@apache.org.
change quotation mark from utf-8 to ascii
Closes #24.
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/135a0c0d
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/135a0c0d
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/135a0c0d
Branch: refs/heads/gh-pages
Commit: 135a0c0d9f799c1f56ddd9d0175dd9b3da1e2e78
Parents: 918605f
Author: Tommy YU <tu...@163.com>
Authored: Tue Dec 15 11:22:45 2015 +0800
Committer: Deron Eriksson <de...@us.ibm.com>
Committed: Tue Dec 15 12:55:20 2015 -0800
----------------------------------------------------------------------
.../SystemML_Language_Reference.html | 9804 ------------------
algorithms-classification.md | 4 +-
algorithms-clustering.md | 20 +-
algorithms-descriptive-statistics.md | 60 +-
algorithms-regression.md | 58 +-
dml-language-reference.md | 94 +-
quick-start-guide.md | 2 +-
7 files changed, 119 insertions(+), 9923 deletions(-)
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